{"id": "2606.18611", "title": "QC-GAN: A Parameter-Efficient Quaternion Conformer GAN for High-Fidelity Speech Enhancement", "abstract": "We propose a parameter-efficient speech enhancement framework, Quaternion Conformer GAN (QC-GAN), which combines a Quaternion Conformer generator with MetricGAN-based training. The Hamilton product encodes the magnitude and phase via structured weight sharing, reducing the number of layer parameters while preserving their interdependencies. A metric-learning discriminator was employed to maximize perceptual quality by optimizing the approximate perceptual evaluation scores. On the VoiceBank+DEMAND dataset, QC-GAN achieved a Perceptual Evaluation of Speech Quality (PESQ) score of 3.48 with only 0.89M parameters, delivering a performance comparable to state-of-the-art models at less than half their size. A 35K-parameter variant achieved a PESQ score of 3.23, surpassing conventional methods with significantly fewer parameters. Evaluation on the DNS-Challenge 3 dataset further confirmed generalization to real-world conditions.", "date": "2026-06-17", "categories": []}
{"id": "2606.19568", "title": "Exploring Feature Extraction Technique Parameters for Acoustic Gunshot Classification", "abstract": "Acoustic gunshot detection is a problem with applications across civilian public safety, military operations, and wildlife conservation, yet the field lacks a rigorous exploration of feature extraction techniques with a focus on generalization to realistic data. The mixed effectiveness of commercial gunshot detection and classification systems indicates an open problem that is not adequately addressed by the current literature. In this paper, we present a systematic investigation of common feature extraction techniques using a dataset of 23,000 gunshot recordings across 85 firearms and 21 calibers. We benchmark three feature extraction techniques with 12 total unique parameter sets using ResNet-18. Our results demonstrate that using the correct feature extraction technique can improve top-1 accuracy by up to 20%, and utilizing the correct parameters for a given feature extraction technique can improve that value by up to 4.7%.", "date": "2026-06-17", "categories": []}
{"id": "2512.15313", "title": "Time-Varying Audio Effect Modeling by End-to-End Adversarial Training", "abstract": "Deep learning has become a standard approach for the modeling of audio effects, yet strictly black-box modeling remains problematic for time-varying systems. Unlike time-invariant effects, training models on devices with internal modulation typically requires the recording or extraction of control signals to ensure the time-alignment required by standard loss functions. This paper introduces a Generative Adversarial Network (GAN) framework to model such effects using only input-output audio recordings, without requiring a modulation signal extraction. We propose a convolutional-recurrent architecture trained via a two-stage strategy: an initial adversarial phase allows the model to learn the distribution of the modulation behavior without strict phase constraints, followed by a supervised fine-tuning phase where a State Prediction Network (SPN) estimates the initial internal states required to synchronize the model with the target. Additionally, a new metric based on chirp-train signals is developed to quantify modulation accuracy. Experiments modeling a vintage hardware phaser demonstrate the method's ability to capture time-varying dynamics in a fully black-box context.", "date": "2025-12-17", "categories": []}
{"id": "2602.01394", "title": "SSNAPS: Audio-Visual Separation of Speech and Background Noise with Diffusion Inverse Sampling", "abstract": "This paper addresses the challenge of audio-visual single-microphone speech separation and enhancement in the presence of real-world environmental noise. Our approach is based on generative inverse sampling, where we model clean speech and ambient noise with dedicated diffusion priors and jointly leverage them to recover all underlying sources. To achieve this, reformulate a recent inverse sampler to match our setting. We evaluate on mixtures of 1, 2, and 3 speakers with noise and show that, despite being entirely unsupervised, our method consistently outperforms leading supervised baselines in WER across all conditions. We further extend our framework to handle off-screen speaker separation. Moreover, the high fidelity of the separated noise component makes it suitable for downstream detection of the acoustic scene. Code and pretrained models will become available upon acceptance. Demo page: https://ssnaps2026.github.io/ssnaps2026/", "date": "2026-02-01", "categories": []}
{"id": "2603.27998", "title": "HRIR-Former: Grid-Free Time-Domain Reconstruction of Head-Related Impulse Responses with a Spatially Encoded Transformer", "abstract": "Individualized head-related impulse responses (HRIRs) enable binaural rendering, but dense per-listener measurements are costly. We address HRIR spatial up-sampling from sparse per-listener measurements: given a few measured HRIRs for a listener, predict HRIRs at unmeasured target directions. Prior learning methods often work in the frequency domain, rely on minimum-phase assumptions or separate timing models, and use a fixed direction grid, which can degrade temporal fidelity and spatial continuity. We propose HRIR-Former, a time-domain, grid-free binaural Transformer for reconstructing HRIRs at arbitrary directions from sparse inputs. It uses sinusoidal spatial features, a Conv1D refinement module, and auxiliary interaural time difference (ITD) and interaural level difference (ILD) heads. On SONICOM, it improves normalized mean squared error (NMSE), cosine distance, and ITD/ILD errors over prior methods; ablations validate modules and show minimum-phase preprocessing is unnecessary.", "date": "2026-03-30", "categories": []}
{"id": "2606.14820", "title": "Spectro-Temporal Interference Confounds Phase Encoding in Spatial Audio Foundation Models", "abstract": "Recent spatial self supervised audio models achieve high performance on localization tasks, raising questions about their encoding of microsecond interaural phase fine structures. We propose a psychoacoustic benchmark based on the binaural masking level difference to evaluate this. Using an equalization cancellation baseline and a GCC PHAT positive control we evaluate nine frozen audio models spanning binaural SSL, monaural SSL, and neural audio codecs. Four monaural negative controls yield zero BMLD confirming binaural specificity. Two general purpose binaural SSL models exhibit minimal phase sensitivity while dedicated binaural spatial SSL models achieve BMLD comparable to the analytical baseline. Progressive physical ablations show that general purpose binaural SSL models rely on spectro temporal interference textures rather than cross channel phase computation. High detection rates in speech reflect a confounding reliance on broadband envelopes rather than genuine phase encoding.", "date": "2026-06-12", "categories": []}
{"id": "2606.12662", "title": "BASENet: Band-Adapted Speech Enhancement Network with Cross-Band Attention", "abstract": "Speech enhancement models typically apply uniform capacity across all frequencies, disregarding the non-uniform spectral resolution of human hearing. We propose BASENet, a frequency-adapted architecture that partitions the spectrum into Bark-scale bands and assigns each a scaled-capacity encoder derived from critical-band density, automatically granting deeper branches to perceptually dense low frequencies and lighter ones to high frequencies. A cross-band attention module captures harmonic dependencies across bands through compact frequency-pooled representations at linear complexity. Built on inverted residual blocks with dense connectivity and a convolutional recurrent network, BASENet achieves 3.55 PESQ and STOI~96% on VoiceBank+DEMAND with only 0.83M parameters and 7.3 G~MACs, the fewest parameters among all methods with PESQ > 3.50. A causal variant (3.44 PESQ) surpasses several non-causal baselines, confirming suitability for real-time streaming on resource-constrained devices.", "date": "2026-06-10", "categories": []}
{"id": "2606.10046", "title": "Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models", "abstract": "Flow-matching transformers achieve strong audio separation, yet their attention dynamics are opaque. We adapt established causal-intervention principles into a deterministic, inference-time probing protocol for SAM Audio. Orthogonal probing uncovers a dual-pathway text-conditioning mechanism: additive injections control semantic identity, while cross-attention refines acoustic structure. We observe an asynchronous layerwise convergence: stable layers build temporal scaffolds early, whereas fast layers continue resolving artifacts during sampling. The model also attenuates temporal segmentation cues to maintain continuous-flow stability. Using these insights, we propose Layer-Selective Attention Caching (LSAC), a training-free acceleration method that caches attention in stable layers. Across acoustic complexities, LSAC cuts self-attention computation by about ~25% with negligible quality loss and yields up to 6.7x higher quality retention than naive step reduction.", "date": "2026-06-08", "categories": []}
{"id": "2606.11828", "title": "Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions", "abstract": "Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy designs to preserve perceptual quality, but the resulting watermarks lack robustness under suppression by speech reconstruction models. Improving robustness is challenging due to the inherent robustness-fidelity trade-off in existing designs, where increasing watermark energy improves robustness but reduces fidelity. To address this problem, we propose a feature-aligned watermarking method that aligns the watermark with the original speech feature distribution, allowing higher watermark energy to improve robustness while preserving imperceptibility. We use a pretrained speech codec to generate a pseudo-speech watermark and fuse it into the spectrogram of the input audio, with VAD loss and perceptual losses guiding embedding within voiced regions. Experiments show that our method maintains imperceptibility comparable to existing approaches while substantially improving robustness under both seen and unseen speech reconstruction models.", "date": "2026-06-10", "categories": []}
{"id": "2606.09677", "title": "MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation", "abstract": "While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening quality. To address this, we propose a novel MeanFlow-based one-step generative corrector (MeCo). MeCo learns a conditional average velocity field to map discriminative estimates directly onto the clean speech manifold in a single step. To maximize one-step generation performance, we introduce Data-Space Optimization (DSO). DSO integrates an $\\mathbf{x}_r$-loss, which penalizes prediction errors on longer displacement intervals to serve as a generative objective for human listening quality, with an Endpoint SI-SDR loss that directly optimizes terminal signal fidelity. Experiments demonstrate that MeCo achieves state-of-the-art (SOTA) performance with minimal computational overhead, simultaneously achieving superior signal fidelity and human listening quality in both in-domain and out-of-domain scenarios.", "date": "2026-06-08", "categories": []}
{"id": "2512.20978", "title": "GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model", "abstract": "Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and high-fidelity speech. We propose GenTSE, a two-stage decoder-only generative LM for TSE: Stage-1 predicts coarse semantic tokens, and Stage-2 generates fine acoustic tokens. Separating semantics and acoustics stabilizes decoding and yields more accurate target speech. Both stages use continuous SSL or codec embeddings, offering richer context than discretized-prompt methods. To reduce exposure bias, we employ a Frozen-LM Conditioning training strategy that conditions the LMs on predicted tokens from earlier checkpoints to reduce the gap between teacher-forcing training and autoregressive inference. We further apply DPO to better align outputs with perceptual preferences. Experiments on Libri2Mix show that GenTSE surpasses previous LM-based systems in speech quality, intelligibility, and speaker consistency.", "date": "2025-12-24", "categories": []}
{"id": "2604.24199", "title": "Speech Enhancement Based on Drifting Models", "abstract": "We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Rather than relying on iterative sampling, DriftSE natively achieves one-step inference by evolving the pushforward distribution of a mapping function to directly match the clean speech distribution. This evolution is driven by a Drifting Field, a learned correction vector that guides samples toward the high-density regions of the clean distribution, which naturally facilitates training on unpaired data by matching distributions rather than paired samples. We investigate the framework under two formulations: a direct mapping from the noisy observation, and a stochastic conditional generative model from a Gaussian prior. Experiments on the VoiceBank-DEMAND benchmark demonstrate that DriftSE achieves high-fidelity enhancement in a single step, outperforming multi-step diffusion baselines and establishing a new paradigm for speech enhancement.", "date": "2026-04-27", "categories": []}
{"id": "2606.06743", "title": "HybridCodec: Fast Dual-Stream, Semantically Enhanced Neural Audio Codec", "abstract": "The popularity of neural audio codecs as speech tokenizers has surged with the advent of Multimodal Large Language Models. New codec architectures with semantic and acoustic disentanglement have emerged. There are two main approaches to introduce semantic information into codec models: one distills semantic information from SSL representations into the first RVQ layer, while the other maintains separate streams for semantic and acoustic features. We propose HybridCodec, a unified architecture that combines both paradigms. It employs separate semantic and acoustic branches while distilling SSL representations into the semantic stream. This design ensures strong disentanglement without requiring an SSL model during inference. HybridCodec shows superior semantic specialization (RVQ-1) on in-domain test set and competitive reconstruction (RVQ-all). We demonstrate its robustness in out-of-domain and zero-shot cross-lingual settings, achieving a 3x speedup over existing dual-stream models.", "date": "2026-06-04", "categories": []}
{"id": "2508.14623", "title": "A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References", "abstract": "This paper examines the implications of using the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) as both evaluation and training objective in supervised speech separation, when the training references contain noise, as is the case with the de facto benchmark WSJ0-2Mix. A derivation of the SI-SDR with noisy references reveals that noise limits the achievable SI-SDR, or leads to undesired noise in the separated outputs. To address this, a method is proposed to enhance references and augment the mixtures with WHAM!, aiming to train models that avoid learning noisy references. Two models trained on these enhanced datasets are evaluated with the non-intrusive NISQA.v2 metric. Results show reduced noise in separated speech but suggest that processing references may introduce artefacts, limiting overall quality gains. Negative correlation is found between SI-SDR and perceived noisiness across models on the WSJ0-2Mix and Libri2Mix test sets, underlining the conclusion from the derivation.", "date": "2025-08-20", "categories": []}
{"id": "2603.07584", "title": "Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations", "abstract": "Computational engine sound modeling is central to the automotive audio industry, particularly for active sound design applications and virtual prototyping. Emerging data-driven engine sound synthesis methods require large volumes of standardized, clean audio recordings with precisely time-aligned operating-state annotations: data that is difficult to obtain due to high costs, specialized measurement equipment requirements, and inevitable noise contamination. We present an analysis-driven framework for generating engine audio with sample-accurate control annotations. The method extracts harmonic structures from real recordings through pitch-adaptive spectral analysis, which then drive an extended parametric harmonic-plus-noise synthesizer. With this framework, we augment 5-10 min of source audio per engine 15-30x via diverse control trajectories and parametric variation, producing the Procedural Engine Sounds Dataset (19.0 h, 5,935 files): a set of engine audio signals with sample-accurate RPM and torque annotations spanning a wide range of operating conditions, signal complexities, and harmonic profiles. Comparison against real recordings validates that the synthesized data preserves characteristic harmonic structures, and a baseline differentiable synthesis network trained on the dataset confirms its suitability for data-driven engine sound modeling. The dataset is released publicly to support research on engine timbre analysis, control parameter estimation, and neural generative synthesis.", "date": "2026-03-08", "categories": []}
{"id": "2603.09391", "title": "Physics-Informed Neural Engine Sound Modeling with Differentiable Pulse-Train Synthesis", "abstract": "Engine sounds originate from sequential exhaust pressure pulses rather than sustained harmonic oscillations. While neural synthesis methods typically aim to approximate the resulting spectral characteristics, we propose directly modeling the underlying pulse shapes and temporal structure. We present the Pulse-Train-Resonator (PTR) model, a differentiable synthesis architecture that generates engine audio as parameterized pulse trains aligned to engine firing patterns and propagates them through recursive Karplus-Strong resonators simulating exhaust acoustics. The architecture integrates physics-informed inductive biases including harmonic decay, thermodynamic pitch modulation, valve-dynamics envelopes, exhaust system resonances and derived engine operating modes such as throttle operation and Deceleration Fuel Cutoff (DFCO). Validated on three diverse engine types totaling 7.5 hours of audio, PTR achieves a 21% improvement in harmonic reconstruction and a 5.7% reduction in total loss over a harmonic-plus-noise baseline model, while providing interpretable parameters corresponding to physical phenomena. Complete code, model weights, and audio examples are openly available.", "date": "2026-03-10", "categories": []}
{"id": "2606.04103", "title": "The Differentiable Auditory Loop (DAL): An ML Framework for Hyper-Personalized Hearing Aids", "abstract": "Conventional hearing aids rely on fixed, frequency-dependent amplification and compression to manage reduced sensitivity, which often fails to provide sufficient listening support in complex environments, such as situations with multiple speakers (the ``cocktail party'' problem). To more comprehensively address the underlying encoding dysfunctions of hearing loss, we introduce the Differentiable Auditory Loop (DAL), a new open-source framework for personalized hearing aid design and fitting. Our first implementation of DAL incorporates CARFAC, a differentiable model of human cochlear function, which we ported to JAX, to optimize a deep neural network to match impaired auditory neural activity patterns with a normal-hearing reference. To build a hearing aid with the fine-grained spectro-temporal signal processing required, we adopt SEANet, a waveform-to-waveform fully convolutional UNet generator. We fine-tune the network by comparing the outputs of a CARFAC model fitted to normal hearing with that of a CARFAC model fitted to match each subject's individual hearing impairment. The comparison is done using loss functions derived from the respective CARFAC neural activity pattern (NAP) outputs and stabilized auditory images (SAIs), the latter providing a 2D representation that captures phase-insensitive temporal structure in the auditory nerve output. Through gradient descent, the SEANet model learns to both denoise the input and compensate for the hearing loss modelled by the impaired CARFAC model. Across neural-representation and signal-fidelity metrics, the DAL-optimized SEANet model outperformed the tested master hearing aid (MHA) baselines. The DAL framework provides a practical path toward model-based, machine-learning-driven personalization of hearing aid signal processing. Next steps include hardware deployment to enable real-world clinical testing.", "date": "2026-06-02", "categories": []}
{"id": "2510.01891", "title": "HRTFformer: A Spatially-Aware Transformer for Individual HRTF Upsampling in Immersive Audio Rendering", "abstract": "Individual Head-Related Transfer Functions (HRTFs) are starting to be introduced in many commercial immersive audio applications and are crucial for realistic spatial audio rendering. However, one of the main hesitations regarding their introduction is that creating individual HRTFs is impractical at scale due to the complexities of the HRTF measurement process. To mitigate this drawback, HRTF spatial upsampling has been proposed with the aim of reducing the measurements required. While prior work has seen success with different machine learning (ML) approaches, these models often struggle with long-range preservation of local spatial variation patterns across neighbouring source directions and generalization at high upsampling factors. In this paper, we propose a novel transformer-based architecture for HRTF upsampling, leveraging the attention mechanism to better capture spatial correlations across the HRTF sphere. Working in the spherical harmonic (SH) domain, our model learns to reconstruct high-resolution HRTFs from sparse input measurements with significantly improved accuracy. To enhance spatial coherence, we introduce a neighbour dissimilarity loss that promotes magnitude smoothness, yielding more realistic upsampling. We evaluate our method using both perceptual localization models and objective spectral distortion metrics. Experiments show that our model outperforms existing methods across several evaluation metrics in generating realistic, high-fidelity HRTFs.", "date": "2025-10-02", "categories": []}
{"id": "2511.13487", "title": "Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization", "abstract": "This study presents a systematic evaluation of time-frequency feature design for binaural sound source localization (SSL), focusing on how feature selection influences model performance across diverse conditions. We investigate the performance of a convolutional neural network (CNN) model using various combinations of amplitude-based features (magnitude spectrogram, interaural level difference - ILD) and phase-based features (phase spectrogram, interaural phase difference - IPD). Evaluations on in-domain and out-of-domain data with mismatched head-related transfer functions (HRTFs) reveal that carefully chosen feature combinations often outperform increases in model complexity. While two-feature sets such as ILD + IPD are sufficient for in-domain SSL, generalization to diverse content requires richer inputs combining channel spectrograms with both ILD and IPD. Using the optimal feature sets, our low-complexity CNN model achieves competitive performance. Our findings underscore the importance of feature design in binaural SSL and provide practical guidance for both domain-specific and general-purpose localization.", "date": "2025-11-17", "categories": []}
{"id": "2605.29531", "title": "Audio Deepfake Detection with Half-Truth Localisation Using Cross-Attentive Feature Fusion", "abstract": "Audio deepfake detection is well-studied as a binary problem, but partially manipulated speech, where a short synthesised segment is spliced into an otherwise genuine utterance, poses a harder and more realistic threat. Detecting such half-truth audio requires not only distinguishing it from real and fully fake speech, but also localising where the manipulation occurs. We present CAFNet, a 576k-parameter architecture that addresses both tasks jointly: it performs ternary classification (real, fully-fake, or half-truth) and regresses the temporal boundaries of the synthesised region in a single forward pass. CAFNet fuses Mel-Frequency Cepstral Coefficient (MFCC), Linear-Frequency Cepstral Coefficient (LFCC), and Chroma Short-Time Fourier Transform (Chroma-STFT) features through parallel depthwise-separable convolution branches with cross-attention, followed by a Bidirectional Long Short-Term Memory (BiLSTM) regression head for boundary prediction. On the combined Multi-Lingual Audio Deepfake Detection Corpus (MLADDC) T2+T3 test set, CAFNet achieves 92.71% accuracy and macro Area Under the Curve (AUC) of 0.9910, with boundary localisation Mean Absolute Error (MAE) of 0.075s and a median error of 0.052s. On binary detection, it achieves 96.76% accuracy and 3.20% Equal Error Rate (EER), outperforming fine-tuned XLS-R 300M (78.31%) and AST 87M (93.03%) at over 500 times fewer parameters. A cross-dataset study further shows that standard fine-tuning collapses cross-domain representations even under reduced backbone learning rates.", "date": "2026-05-28", "categories": []}
{"id": "2605.28101", "title": "EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction", "abstract": "Predicting spatially varying Room Impulse Response (RIR) from sparse observations is a critical but highly challenging inverse problem for immersive spatial audio rendering. In this work, we present EIGENET, a geometry-informed multi-modal framework for few-shot novel view RIR prediction. At its core is a Cross-view Alternate-attention Transformer that iteratively refines local intra-view acoustic structures and global cross-view spatial relationships. We empirically demonstrate that this architecture is capable of making full use of the multi-view multi-modal context while performing spatial-temporal reasoning for RIR prediction. Inspired by acoustic ray tracing, we design a geometry-informed modulation block to formulate the connection between geometric features and RIR power spectrum. In the mean time, an auxiliary loss is introduced to transform the single-target waveform prediction into a multi-task learning framework. Through ablation studies, we demonstrate that this design yields consistent performance gains regardless of the underlying backbone, thereby confirming its foundational utility and architecture-agnostic generalizability for RIR prediction task. Evaluated on both simulated and real-world benchmarks, EIGENET achieves both state-of-the-art performance in few-shot novel view RIR prediction and sim-to-real generalization. Codes and checkpoints are available on https://github.com/FEAfeatherTHER/EigeNet.", "date": "2026-05-27", "categories": []}
{"id": "2605.03929", "title": "PHALAR: Phasors for Learned Musical Audio Representations", "abstract": "Stem retrieval, the task of matching missing stems to a given audio submix, is a key challenge currently limited by models that discard temporal information. We introduce PHALAR, a contrastive framework achieving a relative accuracy increase of up to $\\approx 70\\%$ over the state-of-the-art while requiring $<50\\%$ of the parameters and a 7$\\times$ training speedup. By utilizing a Learned Spectral Pooling layer and a complex-valued head, PHALAR enforces pitch-equivariant and phase-equivariant biases. PHALAR establishes new retrieval state-of-the-art across MoisesDB, Slakh, and ChocoChorales, correlating significantly higher with human coherence judgment than semantic baselines. Finally, zero-shot beat tracking and linear chord probing confirm that PHALAR captures robust musical structures beyond the retrieval task.", "date": "2026-05-05", "categories": []}
{"id": "2605.25648", "title": "StrTransformer: Source-Wise Structured Transformers for Unsupervised Blind Source Recovery", "abstract": "This paper proposes StrTransformer, a source-wise structured Transformer framework for blind source recovery and branch-wise latent modeling. Instead of using an encoder to infer latent variables, StrTransformer directly optimizes the latent source matrix together with an observation-space mixer and source-wise structural Transformer branches. The mixer enforces reconstruction consistency, while each Transformer branch imposes a differentiable structural constraint on one latent source trajectory. Specifically, each source is converted into multi-scale patch tokens, randomly masked, processed by a locality-biased Transformer, and evaluated through a masked patch reconstruction energy. This energy acts as an implicit source-wise structural prior. To encourage different latent branches to specialize into different temporal regimes, StrTransformer further introduces an ordered multi-scale controller that learns branch-specific patch-scale weights, ordered scale centers, and locality attention slopes. The resulting objective combines observation reconstruction, source-wise structural regularization, and modular auxiliary penalties for separation and scale specialization. We analyze the decoupling and coupling structure of the objective, the regularized exact-reconstruction fiber, and the reduction of permutation symmetry induced by ordered branch descriptors. A controlled case study shows that the learned branches converge to distinct temporal-scale structures and recover source-aligned latent trajectories under post-hoc evaluation.", "date": "2026-05-25", "categories": []}
{"id": "2502.04230", "title": "XAttnMark: Learning Robust Audio Watermarking with Cross-Attention", "abstract": "The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data provenance, and the spread of misinformation via deepfake audio. Watermarking offers a proactive solution by embedding imperceptible yet identifiable and traceable signals into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have improved robustness and quality, they struggle to jointly optimize both robust detection and accurate attribution. This paper introduces Cross-Attention Robust Audio Watermark (XATTNMARK), which bridges this gap by leveraging partial parameter sharing between the generator and the detector, a cross-attention mechanism for efficient message retrieval, and a temporal conditioning module for improved message distribution. Additionally, we propose a psychoacoustic-aligned time-frequency (TF) masking loss that captures fine-grained auditory masking effects, improving watermark imperceptibility. XATTNMARK achieves state-of-the-art performance in both detection and attribution, demonstrating superior robustness against a wide range of audio transformations, including challenging generative editing at varying strengths. This work advances audio watermarking for protecting intellectual property and ensuring authenticity in the era of generative AI.", "date": "2025-02-06", "categories": []}
{"id": "2605.03934", "title": "Towards Open World Sound Event Detection", "abstract": "Sound Event Detection (SED) plays a vital role in audio understanding, with applications in surveillance, smart cities, healthcare, and multimedia indexing. However, conventional SED systems operate under a closed-world assumption, limiting their effectiveness in real-world environments where novel acoustic events frequently emerge. Inspired by the success of open-world learning in computer vision, we introduce the Open-World Sound Event Detection (OW-SED) paradigm, where models must detect known events, identify unseen ones, and incrementally learn from them. To tackle the unique challenges of OW-SED, such as overlapping and ambiguous events, we propose a 1D Deformable architecture that leverages deformable attention to adaptively focus on salient temporal regions. Furthermore, we design a novel Open-World Deformable Sound Event Detection Transformer (WOOT) framework incorporating feature disentanglement to separate class-specific and class-agnostic representations, together with a one-to-many matching strategy and a diversity loss to enhance representation diversity. Experimental results demonstrate that our method achieves marginally superior performance compared to existing leading techniques in closed-world settings and significantly improves over existing baselines in open-world scenarios.", "date": "2026-05-05", "categories": []}
{"id": "2605.22262", "title": "Automatic Contextual Audio Denoising", "abstract": "Audio context determines which sound components and sources are relevant and which can be perceived as irrelevant (noise) by listeners. For example, traffic noise is informative in urban surveillance but noise for a phone call at the same location. Most current audio denoising systems apply fixed target-noise definitions, often removing useful components in one context while failing to suppress irrelevant components. To address this, we introduce the concept automatic contextual audio denoising (ACAD) which defines target and noise based on the inferred context. In this work, we restrict context to be associated with an acoustic scene class. We label sound events outside the event distribution of a scene class (noise) as out-of-context (OC) and events typical for that scene as in-context (IC). We implement a deep learning method that automatically infers the context of the audio signal and removes OC components, and benchmark it against variants: without context inference, with oracle context, and with separately provided uninformative context. On paired clean/noisy data across diverse contexts, where OC components in one context may be IC in another, our proposed method outperforms other approaches across standard objective metrics, indicating that the model can infer context and context-dependent processing can enhance denoising.", "date": "2026-05-21", "categories": []}
{"id": "2507.15970", "title": "CIS-BWE: Chaos-Informed Speech Bandwidth Extension", "abstract": "Recovering high-frequency components lost to bandwidth constraints is crucial for applications ranging from telecommunications to high-fidelity audio on limited resources. We introduce NDSI-BWE, a new adversarial Band Width Extension (BWE) framework that leverage four new discriminators inspired by nonlinear dynamical system to capture diverse temporal behaviors: a Multi-Resolution Lyapunov Discriminator (MRLD) for determining sensitivity to initial conditions by capturing deterministic chaos, a Multi-Scale Recurrence Discriminator (MS-RD) for self-similar recurrence dynamics, a Multi-Scale Detrended Fractal Analysis Discriminator (MSDFA) for long range slow variant scale invariant relationship, a Multi-Resolution Poincar\\'e Plot Discriminator (MR-PPD) for capturing hidden latent space relationship, a Multi-Period Discriminator (MPD) for cyclical patterns, a Multi-Resolution Amplitude Discriminator (MRAD) and Multi-Resolution Phase Discriminator (MRPD) for capturing intricate amplitude-phase transition statistics. By using depth-wise convolution at the core of the convolutional block with in each discriminators, NDSI-BWE attains an eight-times parameter reduction. These seven discriminators guide a complex-valued ConformerNeXt based genetor with a dual stream Lattice-Net based architecture for simultaneous refinement of magnitude and phase. The genertor leverage the transformer based conformer's global dependency modeling and ConvNeXt block's local temporal modeling capability. Across six objective evaluation metrics and subjective based texts comprises of five human judges, NDSI-BWE establishes a new SoTA in BWE.", "date": "2025-07-21", "categories": []}
{"id": "2511.21247", "title": "The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval", "abstract": "This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source separation and related MIR tasks within the classical music domain. The dataset is composed of over one hour recordings of musical pieces performed by the Colibr\\`i Ensemble at The Spheres recording studio, capturing two canonical works - Tchaikovsky's Romeo and Juliet and Mozart's Symphony No. 40 - along with chromatic scales and solo excerpts for each instrument. The recording setup employed 23 microphones, including close spot, main, and ambient microphones, enabling the creation of realistic stereo mixes with controlled bleeding and providing isolated stems for supervised training of source separation models. In addition, room impulse responses were estimated for each instrument position, offering valuable acoustic characterization of the recording space. We present the dataset structure, acoustic analysis, and baseline evaluations using X-UMX based models for orchestral family separation and microphone debleeding. Results highlight both the potential and the challenges of source separation in complex orchestral scenarios, underscoring the dataset's value for benchmarking and for exploring new approaches to separation, localization, dereverberation, and immersive rendering of classical music.", "date": "2025-11-26", "categories": []}
{"id": "2512.03637", "title": "AaSP: Aliasing-aware Self-Supervised Pre-Training for Audio Spectrogram Transformers", "abstract": "Transformer-based audio self-supervised learning (SSL) models commonly use spectrograms, vision-style Transformers, and masked modeling objectives. However, convolutional patchification with temporal downsampling lowers the effective Nyquist frequency and introduces aliasing, while na\\\"ive low-pass filtering may remove task-relevant high-frequency cues. We present AaSP, an aliasing-aware self-supervised pre-training framework for audio spectrogram transformers. AaSP combines an aliasing-aware patch representation, teacher-student masked modeling, a cross-attention predictor, and multi-mask contrastive regularization to learn representations that integrate features from alias-prone modulation bands while remaining stable across masked views. Its patch-embedding module, Aliasing-aware Patch Embedding (AaPE), augments standard patch tokens with features from alias-prone modulation bands using a band-limited complex sinusoidal kernel with a two-sided exponential window. The kernel's frequency and decay parameters are estimated from the input, enabling adaptive subband analysis whose outputs are fused with standard patch tokens. We pre-train on AudioSet and evaluate the learned representations by fine-tuning and linear evaluation on acoustic/environmental, speech, and music recognition benchmarks. Under fine-tuning, the full AaSP framework achieves state-of-the-art results on AS-20K, ESC-50, and NSynth among compared self-supervised baselines, while remaining competitive elsewhere. Linear evaluation shows a similar trend, including gains on US8K and NSynth. Overall, AaSP learns representations that are more stable under aliasing-sensitive temporal perturbations and competitive for downstream transfer.", "date": "2025-12-03", "categories": []}
{"id": "2605.12534", "title": "BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations", "abstract": "Most work in audio enhancement targets human speech, while bioacoustics is less studied due to noisy recordings and the distinct traits of animal sounds. To fill this gap, we adapt speech enhancement methods and build BioSEN, a model made for bioacoustic signals. BioSEN has three modules: a multi-scale dual-axis attention unit for time-frequency feature extraction, a bio-harmonic multi-scale enhancement unit for capturing harmonic structures, and an\n  energy-adaptive gating connection unit that uses frequency weights to keep vocalizations from being removed as noise. Tests on three bioacoustic datasets show that BioSEN matches or exceeds state-of-the-art speech enhancement models while using far less computation. These results show BioSEN's strength for bioacoustic audio enhancement and its promise for biodiversity monitoring and conservation.", "date": "2026-05-02", "categories": []}
{"id": "2605.14231", "title": "AudioMosaic: Contrastive Masked Audio Representation Learning", "abstract": "Audio self-supervised learning (SSL) aims to learn general-purpose representations from large-scale unlabeled audio data. While recent advances have been driven mainly by generative reconstruction objectives, contrastive approaches remain less explored, partly due to the difficulty of designing effective audio augmentations and the large batch sizes required for contrastive pre-training. We introduce \\textbf{AudioMosaic}, a contrastive learning-based audio encoder for general audio understanding. During pre-training, AudioMosaic constructs positive pairs by applying structured time-frequency masking to spectrogram patches, which reduces memory usage and enables efficient large-batch training. Compared with generative approaches, the AudioMosaic encoder learns more discriminative utterance-level representations that demonstrate strong transferability across datasets, domains, and acoustic conditions. Extensive experiments show that AudioMosaic achieves state-of-the-art performance on several standard audio benchmarks under both linear probing and fine-tuning. We further show that integrating the pretrained AudioMosaic encoder into audio-language models improves performance on audio-language tasks. The code is publicly available in our \\href{https://github.com/HanxunH/AudioMosaic}{GitHub repository}.", "date": "2026-05-14", "categories": []}
{"id": "2602.01861", "title": "RIR-Former: Coordinate-Guided Transformer for Continuous Reconstruction of Room Impulse Responses", "abstract": "Room impulse responses (RIRs) are essential for many acoustic signal processing tasks, yet measuring them densely across space is often impractical. In this work, we propose RIR-Former, a grid-free, one-step feed-forward model for RIR reconstruction. By introducing a sinusoidal encoding module into a transformer backbone, our method effectively incorporates microphone position information, enabling interpolation at arbitrary array locations. Furthermore, a segmented multi-branch decoder is designed to separately handle early reflections and late reverberation, improving reconstruction across the entire RIR. Experiments on diverse simulated acoustic environments demonstrate that RIR-Former consistently outperforms state-of-the-art baselines in terms of normalized mean square error (NMSE) and cosine distance (CD), under varying missing rates and array configurations. These results highlight the potential of our approach for practical deployment and motivate future work on scaling from randomly spaced linear arrays to complex array geometries, dynamic acoustic scenes, and real-world environments.", "date": "2026-02-02", "categories": []}
{"id": "2605.10256", "title": "A Cold Diffusion Approach for Percussive Dereverberation", "abstract": "Most recent advances in audio dereverberation focus almost exclusively on speech, leaving percussive and drum signals largely unexplored despite their importance in music production. Percussive dereverberation poses distinct challenges due to sharp transients and dense temporal structure. In this work, we propose a cold diffusion framework for dereverberating stereo drum stems (downmixes), modeling reverberation as a deterministic degradation process that progressively transforms anechoic signals into reverberant ones. We investigate two reverse-process parameterizations, Direct (next-state) and a Delta-normalized residual (velocity-style) prediction, and implement the framework using both a UNet and a diffusion Transformer backbone. The models are trained and evaluated on curated datasets comprising both acoustic and electronic drum recordings, with reverberation generated using a combination of synthetic and real room impulse responses. Extensive experiments on in-domain and fully out-of-domain test sets demonstrate that the proposed method consistently outperforms strong score-based and conditional diffusion baselines, evaluated using signal-based and perceptual metrics tailored to percussive audio.", "date": "2026-05-11", "categories": []}
{"id": "2605.07694", "title": "Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation", "abstract": "Single-channel speaker distance estimation has recently achieved centimeter-level accuracy in simulated environments, yet it remains unclear which components of the room impulse response (RIR) the model exploits and how performance depends on the recording conditions. In this work, we decompose simulated RIRs into four variants (full, direct-only, no-late, and no-early) using the mixing time estimated from the echo density function as the boundary between early reflections and late reverberation. We define four calibration scenarios, from fully calibrated (synchronised capture, known source level) to fully uncalibrated (arbitrary onset, unknown level), and evaluate all combinations on a matched dataset. Results show that without time calibration, mean absolute error (MAE) increases to $1.29$ m and the model extracts reverberation-based cues, with early reflections emerging as the most informative component. Further analysis against DRR, $C_{50}$, and $T_{60}$ confirms that estimation accuracy improves with stronger early energy and degrades in highly reverberant environments. When time calibration is available, the model achieves a MAE of $0.14$ m by extracting the propagation delay alone, regardless of the RIR content.", "date": "2026-05-08", "categories": []}
{"id": "2601.20362", "title": "Switchcodec: Adaptive residual-expert sparse quantization for high-fidelity neural audio coding", "abstract": "Recent neural audio compression models often rely on residual vector quantization for high-fidelity coding, but using a fixed number of per-frame codebooks is suboptimal for the wide variability of audio content-especially for signals that are either very simple or highly complex. To address this limitation, we propose SwitchCodec, a neural audio codec based on Residual Experts Vector Quantization (REVQ). REVQ combines a shared quantizer with dynamically routed expert quantizers that are activated according to the input audio, decoupling bitrate from codebook capacity and improving compression efficiency. This design ensures full training and utilization of each quantizer. In addition, a variable-bitrate mechanism adjusts the number of active expert quantizers at inference, enabling multi-bitrate operation without retraining. Experiments demonstrate that SwitchCodec surpasses existing baselines on both objective metrics and subjective listening tests.", "date": "2026-01-28", "categories": []}
{"id": "2605.06189", "title": "Predictive-Generative Drift Decomposition for Speech Enhancement and Separation", "abstract": "We propose a plug-and-play framework for speech enhancement and separation that augments predictive methods with a generative speech prior. Our approach, termed Stochastic Interpolant Prior for Speech (SIPS), builds on stochastic interpolants and leverages their flexibility to bridge predictive and generative modeling. Specifically, we decompose the interpolation dynamics into a task-specific drift and a stochastic denoising component, allowing a predictive estimate to be integrated directly into the generative sampling process. This results in a mathematically grounded framework for combining strong pretrained predictors with the expressive power of generative models. To this end, we train a score model using only clean speech, yielding a degradation-agnostic prior that can be reused across tasks. During inference, the predictor provides a deterministic drift that steers the sampling process toward a task-consistent estimate, while the score model preserves perceptual naturalness. Unlike prior hybrid approaches, which typically rely on architecture-specific conditioning and are tied to particular predictors or degradation settings, SIPS provides a unified framework that generalizes across predictors and additive degradation tasks. We demonstrate its effectiveness for both speech enhancement and speech separation using recent predictors such as SEMamba and FlexIO. The proposed method consistently improves perceptual quality, achieving gains up +1.0 NISQA for speech separation.", "date": "2026-05-07", "categories": []}
{"id": "2605.04547", "title": "Stage-adaptive audio diffusion modeling", "abstract": "Recent progress in diffusion-based audio generation and restoration has substantially improved performance across heterogeneous conditioning regimes, including text-conditioned audio generation and audio-conditioned super-resolution. However, training audio diffusion models remains computationally expensive, and most existing pipelines still rely on static optimization recipes that treat the relative importance of training signals as fixed throughout learning. In this work, we argue that a major source of inefficiency lies in the evolving balance between semantic acquisition and generation-oriented refinement. Early training places stronger emphasis on acquiring condition-aligned semantic structure and coarse global organization, whereas later training increasingly emphasizes temporal consistency, perceptual fidelity, and fine-detail refinement. To characterize this evolving balance, we introduce a progress-based regime variable derived from the training-time slope of an SSL-space discrepancy, which measures semantic progress during training. Based on this signal, we develop three complementary stage-aware mechanisms: decayed SSL guidance for early semantic bootstrapping, self-adaptive timestep sampling driven by the regime variable, and structure-aware regularization activated from convergent grouped organization in parameter space. We evaluate these mechanisms on text-conditioned audio generation and audio-conditioned super-resolution. Across both settings, the proposed stage-aware strategies improve convergence behavior and yield gains on the primary generation and spectral reconstruction metrics over standard static baselines. These results support the view that efficient audio diffusion training can benefit from treating external guidance, internal organization, and optimization emphasis as stage-dependent components rather than fixed ingredients.", "date": "2026-05-06", "categories": []}
{"id": "2508.03448", "title": "SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering", "abstract": "Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster's enhanced outputs over other baselines.", "date": "2025-08-05", "categories": []}
{"id": "2605.00431", "title": "MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation", "abstract": "Although recent video-to-audio (V2A) models excelled at synthesizing semantically plausible sounds from visual inputs, they do not explicitly model room-acoustic effects such as reverberation or room impulse responses (RIRs), and thus offer limited controllability over these effects. However, we hypothesize that such V2A models implicitly have semantic knowledge of the relationship between spatial audio and the corresponding vision cues. In this paper, we revisit a V2A model for the sake of the above, and propose the way to utilize the pretrained model as prior for physically grounded room-acoustic processing. Based on one of the state-of-the-art V2A models, MMAudio, we propose MMAudioReverbs that is a unified framework dealing with i) dereverberation and ii) room impulse response (RIR) estimation without network architectural modification, and fine-tuned on a small dataset. Experimental results showed that audio and visual cues respectively have advantage depending on the type of physical room acoustics. It implies that foundation V2A models can be used for physically grounded room-acoustic analysis.", "date": "2026-05-01", "categories": []}
{"id": "2511.20006", "title": "BERT-APC: A Reference-free Framework for Automatic Pitch Correction via Musical Context Inference", "abstract": "Automatic Pitch Correction (APC) enhances vocal recordings by aligning pitch deviations with intended musical notes. However, existing APC systems either rely on reference pitches, which limits practical applicability, or employ simple pitch estimation algorithms that often fail to preserve expressiveness and naturalness. We propose BERT-APC, a reference-free APC framework that corrects pitch errors while maintaining the expressiveness and naturalness of vocal performances. In BERT-APC, a stationary pitch predictor first estimates the stationary pitch of each note from the detuned singing voice, where stationary pitch is the continuous pitch from the stable region of a note and approximates its perceived pitch. A context-aware note pitch predictor then infers the intended pitch sequence using a repurposed music language model that incorporates musical context. Finally, a note-level correction algorithm fixes pitch errors while preserving intentional deviations for emotional expression. We also introduce a learnable data augmentation strategy that improves robustness by simulating realistic detuning patterns. Compared to two recent singing voice transcription models, BERT-APC demonstrated superior target note pitch prediction, outperforming the second-best model, ROSVOT, by 10.49 percentage points on highly detuned samples in raw pitch accuracy. In the MOS test, BERT-APC achieved the highest quality rating of $4.32 \\pm 0.15$, significantly higher than Auto-Tune ($3.22 \\pm 0.18$) and Melodyne ($3.08 \\pm 0.18$), while maintaining a comparable ability to preserve expressive nuances. To the best of our knowledge, this is the first APC model that leverages a music language model to achieve reference-free pitch correction with symbolic musical context. The corrected audio samples are available at https://joshua-1995.github.io/BERT-APC-Demo/.", "date": "2025-11-25", "categories": []}
{"id": "2604.04973", "title": "StrADiff: A Structured Source-Wise Adaptive Diffusion Framework for Linear and Nonlinear Blind Source Separation", "abstract": "This paper presents StrADiff, a Structured Source-Wise Adaptive Diffusion Framework for unsupervised blind source separation under linear and nonlinear mixing. The framework treats each latent dimension as a source branch and assigns to it an individual adaptive reverse diffusion mechanism, so that latent sources are recovered directly from observed mixtures through a single end-to-end objective, without supervised source labels or separate post-processing. Source-wise generation, structural regularization, and observation-space reconstruction are optimized jointly during training. In this instantiation, a Gaussian process (GP) prior is used as one example of a source-wise structured prior to impose temporal organization on each recovered trajectory; the framework itself is not restricted to GP priors and can in principle incorporate other structured priors. Theoretical components clarify the induced pushforward source law, the sample-level role of the structured prior, the coupling between source recovery and prior adaptation, and a conditional weak recovery statement in an idealized linear low-noise regime. Experiments on linear and nonlinear mixtures show that StrADiff can recover meaningful latent source trajectories in an unsupervised manner, with particularly stable performance in the linear case and moderate degradation under nonlinear mixing. Beyond classical signal separation, a source branch may also be interpreted as an independent, disentangled, or otherwise interpretable explanatory factor under suitable structural assumptions, suggesting a broader route toward structured latent modeling and future identifiable nonlinear representation learning.", "date": "2026-04-04", "categories": []}
{"id": "2509.11717", "title": "CodecSep: Prompt-Driven Universal Sound Separation on Neural Audio Codec Latents", "abstract": "Text-guided sound separation enables flexible audio editing, assistive listening, and open-domain source extraction, but systems such as AudioSep remain too expensive for low-latency edge or codec-mediated deployment. Existing neural audio codec separators are efficient, yet largely restricted to fixed stems or closed taxonomies. We introduce CodecSep, a prompt-driven universal sound separation framework that extracts sources directly in neural audio codec latent space. CodecSep combines a frozen DAC backbone with a lightweight FiLM-conditioned Transformer masker driven by CLAP text embeddings, enabling open-vocabulary separation while preserving codec-native efficiency.\n  Across dnr-v2 and five open-domain benchmarks, CodecSep consistently improves over AudioSep in SI-SDR, remains competitive in ViSQOL, and achieves clear gains in human MOS-LQS. Controlled analyses show that fine-grained prompts outperform coarse labels, and that explicit latent masking is substantially more effective than decoder-style latent generation in codec space. Qualitative diagnostics show that neural audio codec latents retain source-dependent structure, which CodecSep exploits mainly through channel-wise source-conditioned modulation.\n  CodecSep also provides a practical code-stream deployment path. When audio is transmitted as neural audio codec codes, CodecSep maps codes to embeddings, separates directly in codec space, and outputs waveforms or re-quantized codes, avoiding the decode-separate-re-encode loop. In this regime, CodecSep requires only 1.35 GMACs end-to-end: about 54 times less compute than AudioSep in the same pipeline and 25 times lower separator-only compute, with much lower latency and memory. More broadly, CodecSep offers a blueprint for codec-native downstream audio processing.", "date": "2025-09-15", "categories": []}
{"id": "2604.21119", "title": "Materialistic RIR: Material Conditioned Realistic RIR Generation", "abstract": "Rings like gold, thuds like wood! The sound we hear in a scene is shaped not only by the spatial layout of the environment but also by the materials of the objects and surfaces within it. For instance, a room with wooden walls will produce a different acoustic experience from a room with the same spatial layout but concrete walls. Accurately modeling these effects is essential for applications such as virtual reality, robotics, architectural design, and audio engineering. Yet, existing methods for acoustic modeling often entangle spatial and material influences in correlated representations, which limits user control and reduces the realism of the generated acoustics. In this work, we present a novel approach for material-controlled Room Impulse Response (RIR) generation that explicitly disentangles the effects of spatial and material cues in a scene. Our approach models the RIR using two modules: a spatial module that captures the influence of the spatial layout of the scene, and a material module that modulates this spatial RIR according to a user-specified material configuration. This explicitly disentangled design allows users to easily modify the material configuration of a scene and observe its impact on acoustics without altering the spatial structure or scene content. Our model provides significant improvements over prior approaches on both acoustic-based metrics (up to +16% on RTE) and material-based metrics (up to +70%). Furthermore, through a human perceptual study, we demonstrate the improved realism and material sensitivity of our model compared to the strongest baselines.", "date": "2026-04-22", "categories": []}
{"id": "2502.11478", "title": "Throat and acoustic paired speech dataset for deep learning-based speech enhancement", "abstract": "In high-noise environments such as factories, subways, and busy streets, capturing clear speech is challenging. Throat microphones can offer a solution because of their inherent noise-suppression capabilities; however, the passage of sound waves through skin and tissue attenuates high-frequency information, reducing speech clarity. Recent deep learning approaches have shown promise in enhancing throat microphone recordings, but further progress is constrained by the lack of a standard dataset. Here, we introduce the Throat and Acoustic Paired Speech (TAPS) dataset, a collection of paired utterances recorded from 60 native Korean speakers using throat and acoustic microphones. Furthermore, an optimal alignment approach was developed and applied to address the inherent signal mismatch between the two microphones. We tested three baseline deep learning models on the TAPS dataset and found mapping-based approaches to be superior for improving speech quality and restoring content. These findings demonstrate the TAPS dataset's utility for speech enhancement tasks and support its potential as a standard resource for advancing research in throat microphone-based applications.", "date": "2025-02-17", "categories": []}
{"id": "2604.19635", "title": "Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model", "abstract": "While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in real-time applications. Direct adaptation to streaming scenarios often leads to catastrophic inference performance degradation due to the severe mismatch between training and streaming inference. To bridge this gap, we present the first autoregressive (AR) models tailored for streaming TSE. Our approach introduces a Chunk-wise Interleaved Splicing Paradigm that ensures highly efficient and stable streaming inference. To ensure the coherence between the extracted speech segments, we design a historical context refinement mechanism that mitigates boundary discontinuities by leveraging historical information. Experiments on Libri2Mix show that while AR generative baseline exhibits performance degradation at low latencies, our approach maintains 100% stability and superior intelligibility. Furthermore, our streaming results are comparable to or even surpass offline baselines. Additionally, our model achieves a Real-Time-Factor (RTF) of 0.248 on consumer-level GPUs. This work provides empirical evidence that AR generative backbones are viable for latency-sensitive applications through the Chunk-wise Interleaved Splicing Paradigm.", "date": "2026-04-21", "categories": []}
{"id": "2604.17381", "title": "StrEBM: A Structured Latent Energy-Based Model for Blind Source Separation", "abstract": "This paper proposes StrEBM, a structured latent energy-based model for source-wise structured representation learning. The framework is motivated by a broader goal of promoting identifiable and decoupled latent organization by assigning different latent dimensions their own learnable structural biases, rather than constraining the entire latent representation with a single shared energy. In this sense, blind source separation is adopted here as a concrete and verifiable testbed, through which the evolution of latent dimensions toward distinct underlying components can be directly examined. In the proposed framework, latent trajectories are optimized directly together with an observation-generation map and source-wise structural parameters. Each latent dimension is associated with its own energy-based formulation, allowing different latent components to gradually evolve toward distinct source-like roles during training. In the present study, this source-wise energy design is instantiated using Gaussian-process-inspired energies with learnable length-scales, but the framework itself is not restricted to Gaussian processes and is intended as a more general structured latent EBM formulation. Experiments on synthetic multichannel signals under linear and nonlinear mixing settings show that the proposed model can recover source components effectively, providing an initial empirical validation of the framework. At the same time, the study reveals important optimization characteristics, including slow late-stage convergence and reduced stability under nonlinear observation mappings. These findings not only clarify the practical behavior of the current GP-based instantiation, but also establish a basis for future investigation of richer source-wise energy families and more robust nonlinear optimization strategies.", "date": "2026-04-19", "categories": []}
{"id": "2510.09065", "title": "MMAudioSep: Taming Video-to-Audio Generative Model Towards Video/Text-Queried Sound Separation", "abstract": "We introduce MMAudioSep, a generative model for video/text-queried sound separation that is founded on a pretrained video-to-audio model. By leveraging knowledge about the relationship between video/text and audio learned through a pretrained audio generative model, we can train the model more efficiently, i.e., the model does not need to be trained from scratch. We evaluate the performance of MMAudioSep by comparing it to existing separation models, including models based on both deterministic and generative approaches, and find it is superior to the baseline models. Furthermore, we demonstrate that even after acquiring functionality for sound separation via fine-tuning, the model retains the ability for original video-to-audio generation. This highlights the potential of foundational sound generation models to be adopted for sound-related downstream tasks. Our code is available at https://github.com/sony/mmaudiosep.", "date": "2025-10-10", "categories": []}
{"id": "2509.02571", "title": "Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening", "abstract": "This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening (e.g., spatial filtering and binaural rendering), enabling user-parameterized control of the reproduced sound field. Steering vectors have typically been used for representing the spatial response of a microphone array as a function of the look-up direction. The basic algebraic representation of these quantities assuming an idealized environment cannot deal with the scattering effect of the sound field. One may thus collect a discrete set of real steering vectors measured in dedicated facilities and super-resolve (i.e., upsample) them. Recently, physics-aware deep learning methods have been effectively used for this purpose. Such deterministic super-resolution, however, suffers from the overfitting problem due to the non-uniform uncertainty over the measurement space. To solve this problem, we integrate an expressive representation based on the neural field (NF) into the principled probabilistic framework based on the Gaussian process (GP). Specifically, we propose a physics-aware composite kernel that models the directional incoming waves and the subsequent scattering effect. Our comprehensive comparative experiment showed the effectiveness of the proposed method under data insufficiency conditions. In downstream tasks such as speech enhancement and binaural rendering using the simulated data of the SPEAR challenge, the oracle performances were attained with less than ten times fewer measurements.", "date": "2025-08-20", "categories": []}
{"id": "2604.12480", "title": "Audio Source Separation in Reverberant Environments using $\\beta$-divergence based Nonnegative Factorization", "abstract": "In Gaussian model-based multichannel audio source separation, the likelihood of observed mixtures of source signals is parametrized by source spectral variances and by associated spatial covariance matrices. These parameters are estimated by maximizing the likelihood through an Expectation-Maximization algorithm and used to separate the signals by means of multichannel Wiener filtering.\n  We propose to estimate these parameters by applying nonnegative factorization based on prior information on source variances. In the nonnegative factorization, spectral basis matrices can be defined as the prior information. The matrices can be either extracted or indirectly made available through a redundant library that is trained in advance. In a separate step, applying nonnegative tensor factorization, two algorithms are proposed in order to either extract or detect the basis matrices that best represent the power spectra of the source signals in the observed mixtures. The factorization is achieved by minimizing the $\\beta$-divergence through multiplicative update rules. The sparsity of factorization can be controlled by tuning the value of $\\beta$.\n  Experiments show that sparsity, rather than the value assigned to $\\beta$ in the training, is crucial in order to increase the separation performance. The proposed method was evaluated in several mixing conditions. It provides better separation quality with respect to other comparable algorithms.", "date": "2026-04-14", "categories": []}
{"id": "2601.04867", "title": "Gradient-based Optimisation of Modulation Effects", "abstract": "Modulation effects such as phasers, flangers and chorus effects are heavily used in conjunction with the electric guitar. Machine learning based emulation of analog modulation units has been investigated in recent years, but most methods have either been limited to one class of effect or suffer from a high computational cost or latency compared to canonical digital implementations. Here, we build on previous work and present a framework for modelling flanger, chorus and phaser effects based on differentiable digital signal processing. The model is trained in the time-frequency domain, but at inference operates in the time-domain, requiring zero latency. We investigate the challenges associated with gradient-based optimisation of such effects, and show that low-frequency weighting of loss functions avoids convergence to local minima when learning delay times. We show that when trained against analog effects units, sound output from the model is in some cases perceptually indistinguishable from the reference, but challenges still remain for effects with long delay times and feedback.", "date": "2026-01-08", "categories": []}
{"id": "2604.10503", "title": "Cross-Cultural Bias in Mel-Scale Representations: Evidence and Alternatives from Speech and Music", "abstract": "Modern audio systems universally employ mel-scale representations derived from 1940s Western psychoacoustic studies, potentially encoding cultural biases that create systematic performance disparities. We present a comprehensive evaluation of cross-cultural bias in audio front-ends, comparing mel-scale features with learnable alternatives (LEAF, SincNet) and psychoacoustic variants (ERB, Bark, CQT) across speech recognition (11 languages), music analysis (6 collections), and European acoustic scene classification (10 European cities). Our controlled experiments isolate front-end contributions while holding architecture and training protocols minimal and constant. Results demonstrate that mel-scale features yield 31.2% WER for tonal languages compared to 18.7% for non-tonal languages (12.5% gap), and show 15.7% F1 degradation between Western and non-Western music. Alternative representations significantly reduce these disparities: LEAF reduces the speech gap by 34% through adaptive frequency allocation, CQT achieves 52% reduction in music performance gaps, and ERB-scale filtering cuts disparities by 31% with only 1% computational overhead. We also release FairAudioBench, enabling cross-cultural evaluation, and demonstrate that adaptive frequency decomposition offers practical paths toward equitable audio processing. These findings reveal how foundational signal processing choices propagate bias, providing crucial guidance for developing inclusive audio systems.", "date": "2026-04-12", "categories": []}
{"id": "2604.05519", "title": "Active noise cancellation on open-ear smart glasses", "abstract": "Smart glasses are becoming an increasingly prevalent wearable platform, with audio as a key interaction modality. However, hearing in noisy environments remains challenging because smart glasses are equipped with open-ear speakers that do not seal the ear canal. Furthermore, the open-ear design is incompatible with conventional active noise cancellation (ANC) techniques, which rely on an error microphone inside or at the entrance of the ear canal to measure the residual sound heard after cancellation. Here we present the first real-time ANC system for open-ear smart glasses that suppresses environmental noise using only microphones and miniaturized open-ear speakers embedded in the glasses frame. Our low-latency computational pipeline estimates the noise at the ear from an array of eight microphones distributed around the glasses frame and generates an anti-noise signal in real-time to cancel environmental noise. We develop a custom glasses prototype and evaluate it in a user study across 8 environments under mobility in the 100--1000 Hz frequency range, where environmental noise is concentrated. We achieve a mean noise reduction of 9.6 dB without any calibration, and 11.2 dB with a brief user-specific calibration.", "date": "2026-04-07", "categories": []}
{"id": "2511.06458", "title": "EchoMark: Perceptual Acoustic Environment Transfer with Watermark-Embedded Room Impulse Response", "abstract": "Acoustic Environment Matching (AEM) is the task of transferring clean audio into a target acoustic environment, enabling engaging applications such as audio dubbing and auditory immersive virtual reality (VR). Recovering similar room impulse response (RIR) directly from reverberant speech offers more accessible and flexible AEM solution. However, this capability also introduces vulnerabilities of arbitrary ``relocation\" if misused by malicious user, such as facilitating advanced voice spoofing attacks or undermining the authenticity of recorded evidence. To address this issue, we propose EchoMark, the first deep learning-based AEM framework that generates perceptually similar RIRs with embedded watermark. Our design tackle the challenges posed by variable RIR characteristics, such as different durations and energy decays, by operating in the latent domain. By jointly optimizing the model with a perceptual loss for RIR reconstruction and a loss for watermark detection, EchoMark achieves both high-quality environment transfer and reliable watermark recovery. Experiments on diverse datasets validate that EchoMark achieves room acoustic parameter matching performance comparable to FiNS, the state-of-the-art RIR estimator. Furthermore, a high Mean Opinion Score (MOS) of 4.22 out of 5, watermark detection accuracy exceeding 99\\%, and bit error rates (BER) below 0.3\\% collectively demonstrate the effectiveness of EchoMark in preserving perceptual quality while ensuring reliable watermark embedding.", "date": "2025-11-09", "categories": []}
{"id": "2508.00307", "title": "Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD", "abstract": "We introduce a U-net model for 360{\\deg} acoustic source localization formulated as a spherical semantic segmentation task. Rather than regressing discrete direction-of-arrival (DoA) angles, our model segments beamformed audio maps (azimuth & elevation) into regions of active sound presence. Using delay-and-sum (DAS) beamforming on a custom 24-microphone array, we generate signals aligned with drone GPS telemetry to create binary supervision masks. A modified U-Net, trained on frequency-domain representations of these maps, learns to identify spatially distributed source regions while addressing class imbalance via the Tversky loss. Because the network operates on beamformed energy maps, the approach is inherently array-independent and can adapt to different microphone configurations and can be transferred to different microphone configurations with minimal adaptation. The segmentation outputs are post-processed by computing centroids over activated regions, enabling robust DoA estimates. Our dataset includes real-world open-field recordings of a DJI Air 3 drone, synchronized with 360{\\deg} video and flight logs across multiple dates and locations. Experimental results show that U-net generalizes across environments, providing improved angular precision, offering a new paradigm for dense spatial audio understanding beyond traditional Sound Source Localization (SSL). We additionally validate the same beamforming-plus-segmentation formulation on the DCASE 2019 TAU Spatial Sound Events benchmark, showing that the approach generalizes beyond drone acoustics to multiclass Sound Event Localization and Detection (SELD) scenarios.", "date": "2025-08-01", "categories": []}
{"id": "2603.25776", "title": "SAHMM-VAE: A Source-Wise Adaptive Hidden Markov Prior Variational Autoencoder for Unsupervised Blind Source Separation", "abstract": "We propose SAHMM-VAE, a source-wise adaptive Hidden Markov prior variational autoencoder for unsupervised blind source separation. Instead of treating the latent prior as a single generic regularizer, the proposed framework assigns each latent dimension its own adaptive regime-switching prior, so that different latent dimensions are pulled toward different source-specific temporal organizations during training. Under this formulation, source separation is not implemented as an external post-processing step; it is embedded directly into variational learning itself. The encoder, decoder, posterior parameters, and source-wise prior parameters are optimized jointly, where the encoder progressively learns an inference map that behaves like an approximate inverse of the mixing transformation, while the decoder plays the role of the generative mixing model. Through this coupled optimization, the gradual alignment between posterior source trajectories and heterogeneous HMM priors becomes the mechanism through which different latent dimensions separate into different source components. To instantiate this idea, we develop three branches within one common framework: a Gaussian-emission HMM prior, a Markov-switching autoregressive HMM prior, and an HMM state-flow prior with state-wise autoregressive flow transformations. Experiments show that the proposed framework achieves unsupervised source recovery while also learning meaningful source-wise switching structures. More broadly, the method extends our structured-prior VAE line from smooth, mixture-based, and flow-based latent priors to adaptive switching priors, and provides a useful basis for future work on interpretable and potentially identifiable latent source modeling.", "date": "2026-03-26", "categories": []}
{"id": "2507.14237", "title": "U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model", "abstract": "This paper explores the outcome of training state-of-the-art dereverberation models with supervision settings ranging from weakly-supervised to virtually unsupervised, relying solely on reverberant signals and an acoustic model for training. Most of the existing deep learning approaches typically require paired dry and reverberant data, which are difficult to obtain in practice. We develop instead a sequential learning strategy motivated by a maximum-likelihood formulation of the dereverberation problem, wherein acoustic parameters and dry signals are estimated from reverberant inputs using deep neural networks, guided by a reverberation matching loss. Our most data-efficient variant requires only 100 reverberation-parameter-labeled samples to outperform an unsupervised baseline, demonstrating the effectiveness and practicality of the proposed method in low-resource scenarios.", "date": "2025-07-17", "categories": []}
{"id": "2603.23947", "title": "Variable-Length Audio Fingerprinting", "abstract": "Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their originals through similar fingerprints. Existing deep learning approaches rigidly fingerprint fixed-length audio segments, thereby neglecting temporal dynamics during segmentation. To address limitations due to this rigidity, we propose Variable-Length Audio FingerPrinting (VLAFP), a novel method that supports variable-length fingerprinting. To the best of our knowledge, VLAFP is the first deep audio fingerprinting model capable of processing audio of variable length, for both training and testing. Our experiments show that VLAFP outperforms existing state-of-the-arts in live audio identification and audio retrieval across three real-world datasets.", "date": "2026-03-25", "categories": []}
{"id": "2603.24283", "title": "Bridging Biological Hearing and Neuromorphic Computing: End-to-End Time-Domain Audio Signal Processing with Reservoir Computing", "abstract": "Despite the advancements in cutting-edge technologies, audio signal processing continues to pose challenges and lacks the precision of a human speech processing system. To address these challenges, we propose a novel approach to simplify audio signal processing by leveraging time-domain techniques and reservoir computing. Through our research, we have developed a real-time audio signal processing system by simplifying audio signal processing through the utilization of reservoir computers, which are significantly easier to train.\n  Feature extraction is a fundamental step in speech signal processing, with Mel Frequency Cepstral Coefficients (MFCCs) being a dominant choice due to their perceptual relevance to human hearing. However, conventional MFCC extraction relies on computationally intensive time-frequency transformations, limiting efficiency in real-time applications. To address this, we propose a novel approach that leverages reservoir computing to streamline MFCC extraction. By replacing traditional frequency-domain conversions with convolution operations, we eliminate the need for complex transformations while maintaining feature discriminability. We present an end-to-end audio processing framework that integrates this method, demonstrating its potential for efficient and real-time speech analysis. Our results contribute to the advancement of energy-efficient audio processing technologies, enabling seamless deployment in embedded systems and voice-driven applications. This work bridges the gap between biologically inspired feature extraction and modern neuromorphic computing, offering a scalable solution for next-generation speech recognition systems.", "date": "2026-03-25", "categories": []}
{"id": "2603.21684", "title": "LipsAM: Lipschitz-Continuous Amplitude Modifier for Audio Signal Processing and its Application to Plug-and-Play Dereverberation", "abstract": "The robustness of deep neural networks (DNNs) can be certified through their Lipschitz continuity, which has made the construction of Lipschitz-continuous DNNs an active research field. However, DNNs for audio processing have not been a major focus due to their poor compatibility with existing results. In this paper, we consider the amplitude modifier (AM), a popular architecture for handling audio signals, and propose its Lipschitz-continuous variants, which we refer to as LipsAM. We prove a sufficient condition for an AM to be Lipschitz continuous and propose two architectures as examples of LipsAM. The proposed architectures were applied to a Plug-and-Play algorithm for speech dereverberation, and their improved stability is demonstrated through numerical experiments.", "date": "2026-03-23", "categories": []}
{"id": "2603.19176", "title": "Few-shot Acoustic Synthesis with Multimodal Flow Matching", "abstract": "Generating audio that is acoustically consistent with a scene is essential for immersive virtual environments. Recent neural acoustic field methods enable spatially continuous sound rendering but remain scene-specific, requiring dense audio measurements and costly training for each environment. Few-shot approaches improve scalability across rooms but still rely on multiple recordings and, being deterministic, fail to capture the inherent uncertainty of scene acoustics under sparse context. We introduce flow-matching acoustic generation (FLAC), a probabilistic method for few-shot acoustic synthesis that models the distribution of plausible room impulse responses (RIRs) given minimal scene context. FLAC leverages a diffusion transformer trained with a flow-matching objective to generate RIRs at arbitrary positions in novel scenes, conditioned on spatial, geometric, and acoustic cues. FLAC outperforms state-of-the-art eight-shot baselines with one-shot on both the AcousticRooms and Hearing Anything Anywhere datasets. To complement standard perceptual metrics, we further introduce AGREE, a joint acoustic-geometry embedding, enabling geometry-consistent evaluation of generated RIRs through retrieval and distributional metrics. This work is the first to apply generative flow matching to explicit RIR synthesis, establishing a new direction for robust and data-efficient acoustic synthesis.", "date": "2026-03-19", "categories": []}
{"id": "2603.14441", "title": "AR-Flow VAE: A Structured Autoregressive Flow Prior Variational Autoencoder for Unsupervised Blind Source Separation", "abstract": "Blind source separation (BSS) seeks to recover latent source signals from observed mixtures. Variational autoencoders (VAEs) offer a natural perspective for this problem: the latent variables can be interpreted as source components, the encoder can be viewed as a demixing mapping from observations to sources, and the decoder can be regarded as a remixing process from inferred sources back to observations. In this work, we propose AR-Flow VAE, a novel VAE-based framework for BSS in which each latent source is endowed with a parameter-adaptive autoregressive flow prior. This prior significantly enhances the flexibility of latent source modeling, enabling the framework to capture complex non-Gaussian behaviors and structured dependencies, such as temporal correlations, that are difficult to represent with conventional priors. In addition, the structured prior design assigns distinct priors to different latent dimensions, thereby encouraging the latent components to separate into different source signals under heterogeneous prior constraints. Experimental results validate the effectiveness of the proposed architecture for blind source separation. More importantly, this work provides a foundation for future investigations into the identifiability and interpretability of AR-Flow VAE.", "date": "2026-03-15", "categories": []}
{"id": "2603.16926", "title": "Music Source Restoration with Ensemble Separation and Targeted Reconstruction", "abstract": "The Inaugural Music Source Restoration (MSR) Challenge targets the recovery of original, unprocessed stems from fully mixed and mastered music. Unlike conventional music source separation, MSR requires reversing complex production processes such as equalization, compression, reverberation, and other real-world degradations. To address MSR, we propose a two-stage system. First, an ensemble of pre-trained separation models produces preliminary source estimates. Then a set of pre-trained BSRNN-based restoration models performs targeted reconstruction to refine these estimates. On the official MSR benchmark, our system surpasses the baselines on all metrics, ranking second among all submissions. The code is available at https://github.com/xinghour/Music-source-restoration-CUPAudioGroup", "date": "2026-03-13", "categories": []}
{"id": "2603.17025", "title": "Shared Representation Learning for Reference-Guided Targeted Sound Detection", "abstract": "Human listeners exhibit the remarkable ability to segregate a desired sound from complex acoustic scenes through selective auditory attention, motivating the study of Targeted Sound Detection (TSD). The task requires detecting and localizing a target sound in a mixture when a reference audio of that sound is provided. Prior approaches, rely on generating a sound-discriminative conditional embedding vector for the reference and pairing it with a mixture encoder, jointly optimized with a multi-task learning approach. In this work, we propose a unified encoder architecture that processes both the reference and mixture audio within a shared representation space, promoting stronger alignment while reducing architectural complexity. This design choice not only simplifies the overall framework but also enhances generalization to unseen classes. Following the multi-task training paradigm, our method achieves substantial improvements over prior approaches, surpassing existing methods and establishing a new state-of-the-art benchmark for targeted sound detection, with a segment-level F1 score of 83.15% and an overall accuracy of 95.17% on the URBAN-SED dataset.", "date": "2026-03-17", "categories": []}
{"id": "2510.17512", "title": "AWARE: Audio Watermarking with Adversarial Resistance to Edits", "abstract": "Prevailing practice in learning-based audio watermarking is to pursue robustness by expanding the set of simulated distortions during training. However, such surrogates are narrow and prone to overfitting. This paper presents AWARE (Audio Watermarking with Adversarial Resistance to Edits), an alternative approach that avoids reliance on attack-simulation stacks and handcrafted differentiable distortions. Embedding is obtained through adversarial optimization in the time-frequency domain under a level-proportional perceptual budget. Detection employs a time-order-agnostic detector with a Bitwise Readout Head (BRH) that aggregates temporal evidence into one score per watermark bit, enabling reliable watermark decoding even under desynchronization and temporal cuts. Empirically, AWARE attains high audio quality and speech intelligibility (PESQ/STOI) and consistently low BER across various audio edits, often surpassing representative state-of-the-art learning-based systems.", "date": "2025-10-20", "categories": []}
{"id": "2603.14917", "title": "Spectrogram features for audio and speech analysis", "abstract": "Spectrogram-based representations have grown to dominate the feature space for deep learning audio analysis systems, and are often adopted for speech analysis also. Initially, the primary motivator for spectrogram-based representations was their ability to present sound as a two dimensional signal in the time-frequency plane, which not only provides an interpretable physical basis for analysing sound, but also unlocks the use of a wide range of machine learning techniques such as convolutional neural networks, that had been developed for image processing. A spectrogram is a matrix characterised by the resolution and span of its two dimensions, as well as by the representation and scaling of each element. Many possibilities for these three characteristics have been explored by researchers across numerous application areas, with different settings showing affinity for various tasks. This paper reviews the use of spectrogram-based representations and surveys the state-of-the-art to question how front-end feature representation choice allies with back-end classifier architecture for different tasks.", "date": "2026-03-16", "categories": []}
{"id": "2603.12837", "title": "Mask2Flow-TSE: Two-Stage Target Speaker Extraction with Masking and Flow Matching", "abstract": "Target speaker extraction (TSE) extracts the target speaker's voice from overlapping speech mixtures given a reference utterance. Existing approaches typically fall into two categories: discriminative and generative. Discriminative methods apply time-frequency masking for fast inference but often over-suppress the target signal, while generative methods synthesize high-quality speech at the cost of numerous iterative steps. We propose Mask2Flow-TSE, a two-stage framework combining the strengths of both paradigms. The first stage applies discriminative masking for coarse separation, and the second stage employs flow matching to refine the output toward target speech. Unlike generative approaches that synthesize speech from Gaussian noise, our method starts from the masked spectrogram, enabling high-quality reconstruction in a single inference step. Experiments show that Mask2Flow-TSE achieves comparable performance to existing generative TSE methods with approximately 85M parameters.", "date": "2026-03-13", "categories": []}
{"id": "2603.11589", "title": "Toward Complex-Valued Neural Networks for Waveform Generation", "abstract": "Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT-based vocoders have recently gained attention. They predict a complex-valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling stages that can increase computational cost. However, current approaches use real-valued networks that process the real and imaginary parts independently. This separation limits their ability to capture the inherent structure of complex spectrograms. We present ComVo, a Complex-valued neural Vocoder whose generator and discriminator use native complex arithmetic. This enables an adversarial training framework that provides structured feedback in complex-valued representations. To guide phase transformations in a structured manner, we introduce phase quantization, which discretizes phase values and regularizes the training process. Finally, we propose a block-matrix computation scheme to improve training efficiency by reducing redundant operations. Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real-valued baselines, and that its block-matrix scheme reduces training time by 25%. Audio samples and code are available at https://hs-oh-prml.github.io/ComVo/.", "date": "2026-03-12", "categories": []}
{"id": "2509.23610", "title": "Efficient Audio-Visual Speech Separation with Discrete Lip Semantics and Multi-Scale Global-Local Attention", "abstract": "Audio-visual speech separation (AVSS) methods leverage visual cues to extract target speech and have demonstrated strong separation quality in noisy acoustic environments. However, these methods usually involve a large number of parameters and require high computational cost, which is unacceptable in many applications where speech separation serves as only a preprocessing step for further speech processing. To address this issue, we propose an efficient AVSS method, named Dolphin. For visual feature extraction, we develop DP-LipCoder, a dual-path lightweight video encoder that transforms lip-motion into discrete audio-aligned semantic tokens. For audio separation, we construct a lightweight encoder-decoder separator, in which each layer incorporates a global-local attention (GLA) block to efficiently capture multi-scale dependencies. Experiments on three benchmark datasets showed that Dolphin not only surpassed the current state-of-the-art (SOTA) model in separation quality but also achieved remarkable improvements in efficiency: over 50% fewer parameters, more than 2.4x reduction in MACs, and over 6x faster GPU inference speed. These results indicate that Dolphin offers a practical and deployable solution for high-performance AVSS in real-world scenarios. Our code and demo page are publicly available at http://cslikai.cn/Dolphin/.", "date": "2025-09-28", "categories": []}
{"id": "2510.12947", "title": "HyWA: Hypernetwork Weight Adapting Personalized Voice Activity Detection", "abstract": "Personalized Voice Activity Detection (PVAD) systems activate only in response to a specific target speaker. Speaker-conditioning methods are employed to inject information about the target speaker into a VAD pipeline, to achieve personalization. Existing speaker-conditioning methods typically modify the inputs or activations of a VAD model. We propose an alternative perspective to speaker conditioning. Our approach, HyWA, employs a hypernetwork to generate personalized weights for a few selected layers of a standard VAD model. We evaluate HyWA against multiple baseline speaker-conditioning techniques using a fixed backbone VAD. Our comparison shows consistent improvements in PVAD performance. This new approach improves the current speaker-conditioning techniques in two ways: i) increases the mean average precision, ii) facilitates deployment by reusing the same VAD architecture.", "date": "2025-10-14", "categories": []}
{"id": "2603.10465", "title": "MoXaRt: Audio-Visual Object-Guided Sound Interaction for XR", "abstract": "In Extended Reality (XR), complex acoustic environments often overwhelm users, compromising both scene awareness and social engagement due to entangled sound sources. We introduce MoXaRt, a real-time XR system that uses audio-visual cues to separate these sources and enable fine-grained sound interaction. MoXaRt's core is a cascaded architecture that performs coarse, audio-only separation in parallel with visual detection of sources (e.g., faces, instruments). These visual anchors then guide refinement networks to isolate individual sources, separating complex mixes of up to 5 concurrent sources (e.g., 2 voices + 3 instruments) with ~2 second processing latency. We validate MoXaRt through a technical evaluation on a new dataset of 30 one-minute recordings featuring concurrent speech and music, and a 22-participant user study. Empirical results indicate that our system significantly enhances speech intelligibility, yielding a 36.2% (p < 0.01) increase in listening comprehension within adversarial acoustic environments while substantially reducing cognitive load (p < 0.001), thereby paving the way for more perceptive and socially adept XR experiences.", "date": "2026-03-11", "categories": []}
{"id": "2603.10701", "title": "AlphaFlowTSE: One-Step Generative Target Speaker Extraction via Conditional AlphaFlow", "abstract": "In target speaker extraction (TSE), we aim to recover target speech from a multi-talker mixture using a short enrollment utterance as reference. Recent studies on diffusion and flow-matching generators have improved target-speech fidelity. However, multi-step sampling increases latency, and one-step solutions often rely on a mixture-dependent time coordinate that can be unreliable for real-world conversations. We present AlphaFlowTSE, a one-step conditional generative model trained with a Jacobian-vector product (JVP)-free AlphaFlow objective. AlphaFlowTSE learns mean-velocity transport along a mixture-to-target trajectory starting from the observed mixture, eliminating auxiliary mixing-ratio prediction, and stabilizes training by combining flow matching with an interval-consistency teacher-student target. Experiments on Libri2Mix and REAL-T confirm that AlphaFlowTSE improves target-speaker similarity and real-mixture generalization for downstream automatic speech recognition (ASR).", "date": "2026-03-11", "categories": []}
{"id": "2603.09187", "title": "The Costs of Reproducibility in Music Separation Research: a Replication of Band-Split RNN", "abstract": "Music source separation is the task of isolating the instrumental tracks from a music song. Despite its spectacular recent progress, the trend towards more complex architectures and training protocols exacerbates reproducibility issues. The band-split recurrent neural networks (BSRNN) model is promising in this regard, since it yields close to state-of-the-art results on public datasets, and requires reasonable resources for training. Unfortunately, it is not straightforward to reproduce since its full code is not available. In this paper, we attempt to replicate BSRNN as closely as possible to the original paper through extensive experiments, which allows us to conduct a critical reflection on this reproducibility issue. Our contributions are three-fold. First, this study yields several insights on the model design and training pipeline, which sheds light on potential future improvements. In particular, since we were unsuccessful in reproducing the original results, we explore additional variants that ultimately yield an optimized BSRNN model, whose performance largely improves that of the original. Second, we discuss reproducibility issues from both methodological and practical perspectives. We notably underline how substantial time and energy costs could have been saved upon availability of the full pipeline. Third, our code and pre-trained models are released publicly to foster reproducible research. We hope that this study will contribute to spread awareness on the importance of reproducible research in the music separation community, and help promoting more transparent and sustainable practices.", "date": "2026-03-10", "categories": []}
{"id": "2603.07285", "title": "Fast and Flexible Audio Bandwidth Extension via Vocos", "abstract": "We propose a Vocos-based bandwidth extension model that enhances audio at 8-48 kHz by generating missing high-frequency content. Inputs are resampled to 48 kHz and processed by a neural vocoder backbone, enabling a single network to support arbitrary upsampling ratios. A lightweight Linkwitz-Riley-inspired refiner merges the original low band with the generated high frequencies via a smooth crossover. On validation, the model achieves competitive log-spectral distance while running at a real-time factor of 0.0001 on an NVIDIA A100 GPU and 0.0053 on an 8-core CPU, demonstrating practical, high-quality BWE at extreme throughput.", "date": "2026-03-07", "categories": []}
{"id": "2603.07471", "title": "Towards Lightweight Adaptation of Speech Enhancement Models in Real-World Environments", "abstract": "Recent studies have shown that post-deployment adaptation can improve the robustness of speech enhancement models in unseen noise conditions. However, existing methods often incur prohibitive computational and memory costs, limiting their suitability for on-device deployment. In this work, we investigate model adaptation in realistic settings with dynamic acoustic scene changes and propose a lightweight framework that augments a frozen backbone with low-rank adapters updated via self-supervised training. Experiments on sequential scene evaluations spanning 111 environments across 37 noise types and three signal-to-noise ratio ranges, including the challenging [-8, 0] dB range, show that our method updates fewer than 1% of the base model's parameters while achieving an average 1.51 dB SI-SDR improvement within only 20 updates per scene. Compared to state-of-the-art approaches, our framework achieves competitive or superior perceptual quality with smoother and more stable convergence, demonstrating its practicality for lightweight on-device adaptation of speech enhancement models under real-world acoustic conditions.", "date": "2026-03-08", "categories": []}
{"id": "2603.00395", "title": "Fine-grained Soundscape Control for Augmented Hearing", "abstract": "Hearables are becoming ubiquitous, yet their sound controls remain blunt: users can either enable global noise suppression or focus on a single target sound. Real-world acoustic scenes, however, contain many simultaneous sources that users may want to adjust independently. We introduce Aurchestra, the first system to provide fine-grained, real-time soundscape control on resource-constrained hearables. Our system has two key components: (1) a dynamic interface that surfaces only active sound classes and (2) a real-time, on-device multi-output extraction network that generates separate streams for each selected class, achieving robust performance for upto 5 overlapping target sounds, and letting users mix their environment by customizing per-class volumes, much like an audio engineer mixes tracks. We optimize the model architecture for multiple compute-limited platforms and demonstrate real-time performance on 6 ms streaming audio chunks. Across real-world environments in previously unseen indoor and outdoor scenarios, our system enables expressive per-class sound control and achieves substantial improvements in target-class enhancement and interference suppression. Our results show that the world need not be heard as a single, undifferentiated stream: with Aurchestra, the soundscape becomes truly programmable.", "date": "2026-02-28", "categories": []}
{"id": "2507.09768", "title": "Knowing When to Quit: Probabilistic Early Exits for Speech Separation", "abstract": "In recent years, deep learning-based single-channel speech separation has improved considerably, in large part driven by increasingly compute- and parameter-efficient neural network architectures. Most such architectures are, however, designed with a fixed compute and parameter budget and consequently cannot scale to varying compute demands or resources, which limits their use in embedded and heterogeneous devices such as mobile phones and hearables. To enable such use-cases we design a neural network architecture for speech separation and enhancement capable of early-exit, and we propose an uncertainty-aware probabilistic framework to jointly model the clean speech signal and error variance which we use to derive probabilistic early-exit conditions in terms of desired signal-to-noise ratios. We evaluate our methods on both speech separation and enhancement tasks where we demonstrate that early-exit capabilities can be introduced without compromising reconstruction, and that when trained on variable-length audio our early-exit conditions are well-calibrated and lead to considerable compute savings when used to dynamically scale compute at test time while remaining directly interpretable.", "date": "2025-07-13", "categories": []}
{"id": "2603.04032", "title": "Multi-Stage Music Source Restoration with BandSplit-RoFormer Separation and HiFi++ GAN", "abstract": "Music Source Restoration (MSR) targets recovery of original, unprocessed instrument stems from fully mixed and mastered audio, where production effects and distribution artifacts violate common linear-mixture assumptions. This technical report presents the CP-JKU team's system for the MSR ICASSP Challenge 2025. Our approach decomposes MSR into separation and restoration. First, a single BandSplit-RoFormer separator predicts eight stems plus an auxiliary other stem, and is trained with a three-stage curriculum that progresses from 4-stem warm-start fine-tuning (with LoRA) to 8-stem extension via head expansion. Second, we apply a HiFi++ GAN waveform restorer trained as a generalist and then specialized into eight instrument-specific experts.", "date": "2026-03-04", "categories": []}
{"id": "2603.04122", "title": "FastWave: Optimized Diffusion Model for Audio Super-Resolution", "abstract": "Audio Super-Resolution is a set of techniques aimed at high-quality estimation of the given signal as if it would be sampled with higher sample rate. Among suggested methods there are diffusion and flow models (which are considered slower), generative adversarial networks (which are considered faster), however both approaches are currently presented by high-parametric networks, requiring high computational costs both for training and inference. We propose a solution to both these problems by re-considering the recent advances in the training of diffusion models and applying them to super-resolution from any to 48 kHz sample rate. Our approach shows better results than NU-Wave 2 and is comparable to state-of-the-art models. Our model called FastWave has around 50 GFLOPs of computational complexity and 1.3 M parameters and can be trained with less resources and significantly faster than the majority of recently proposed diffusion- and flow-based solutions. The code has been made publicly available.", "date": "2026-03-04", "categories": []}
{"id": "2401.01255", "title": "On the Parameter Estimation of Sinusoidal Models for Speech and Audio Signals", "abstract": "In this paper, we examine the parameter estimation performance of three well-known sinusoidal models for speech and audio. The first one is the standard Sinusoidal Model (SM), which is based on the Fast Fourier Transform (FFT). The second is the Exponentially Damped Sinusoidal Model (EDSM) which has been proposed in the last decade, and utilizes a subspace method for parameter estimation, and finally the extended adaptive Quasi-Harmonic Model (eaQHM), which has been recently proposed for AM-FM decomposition, and estimates the signal parameters using Least Squares on a set of basis function that are adaptive to the local characteristics of the signal. The parameter estimation of each model is briefly described and its performance is compared to the others in terms of signal reconstruction accuracy versus window size on a variety of synthetic signals and versus the number of sinusoids on real signals. The latter include highly non stationary signals, such as singing voices and guitar solos. The advantages and disadvantages of each model are presented via synthetic signals and then the application on real signals is discussed. Conclusively, eaQHM outperforms EDS in medium-to-large window size analysis, whereas EDSM yields higher reconstruction values for smaller analysis window sizes. Thus, a future research direction appears to be the merge of adaptivity of the eaQHM and parameter estimation robustness of the EDSM in a new paradigm for high-quality analysis and resynthesis of general audio signals.", "date": "2024-01-02", "categories": []}
{"id": "2603.02022", "title": "CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space", "abstract": "Speech Bandwidth Extension improves clarity and intelligibility by restoring/inferring appropriate high-frequency content for low-bandwidth speech. Existing methods often rely on spectrogram or waveform modeling, which can incur higher computational cost and have limited high-frequency fidelity. Neural audio codecs offer compact latent representations that better preserve acoustic detail, yet accurately recovering high-resolution latent information remains challenging due to representation mismatch. We present CodecFlow, a neural codec-based BWE framework that performs efficient speech reconstruction in a compact latent space. CodecFlow employs a voicing-aware conditional flow converter on continuous codec embeddings and a structure-constrained residual vector quantizer to improve latent alignment stability. Optimized end-to-end, CodecFlow achieves strong spectral fidelity and enhanced perceptual quality on 8 kHz to 16 kHz and 44.1 kHz speech BWE tasks.", "date": "2026-03-02", "categories": []}
{"id": "2603.02794", "title": "Differentiable Time-Varying IIR Filtering for Real-Time Speech Denoising", "abstract": "We present TVF (Time-Varying Filtering), a low-latency speech enhancement model with 1 million parameters. Combining the interpretability of Digital Signal Processing (DSP) with the adaptability of deep learning, TVF bridges the gap between traditional filtering and modern neural speech modeling. The model utilizes a lightweight neural network backbone to predict the coefficients of a differentiable 35-band IIR filter cascade in real time, allowing it to adapt dynamically to non-stationary noise. Unlike ``black-box'' deep learning approaches, TVF offers a completely interpretable processing chain, where spectral modifications are explicit and adjustable. We demonstrate the efficacy of this approach on a speech denoising task using the Valentini-Botinhao dataset and compare the results to a static DDSP approach and a fully deep-learning-based solution, showing that TVF achieves effective adaptation to changing noise conditions.", "date": "2026-03-03", "categories": []}
{"id": "2603.02178", "title": "Reservoir Subspace Injection for Online ICA under Top-n Whitening", "abstract": "Reservoir expansion can improve online independent component analysis (ICA) under nonlinear mixing, yet top-$n$ whitening may discard injected features. We formalize this bottleneck as \\emph{reservoir subspace injection} (RSI): injected features help only if they enter the retained eigenspace without displacing passthrough directions. RSI diagnostics (IER, SSO, $\\rho_x$) identify a failure mode in our top-$n$ setting: stronger injection increases IER but crowds out passthrough energy ($\\rho_x: 1.00\\!\\rightarrow\\!0.77$), degrading SI-SDR by up to $2.2$\\,dB. A guarded RSI controller preserves passthrough retention and recovers mean performance to within $0.1$\\,dB of baseline $1/N$ scaling. With passthrough preserved, RE-OICA improves over vanilla online ICA by $+1.7$\\,dB under nonlinear mixing and achieves positive SI-SDR$_{\\mathrm{sc}}$ on the tested super-Gaussian benchmark ($+0.6$\\,dB).", "date": "2026-03-02", "categories": []}
{"id": "2410.01469", "title": "TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation", "abstract": "In recent years, much speech separation research has focused primarily on improving model performance. However, for low-latency speech processing systems, high efficiency is equally important. Therefore, we propose a speech separation model with significantly reduced parameters and computational costs: Time-frequency Interleaved Gain Extraction and Reconstruction network (TIGER). TIGER leverages prior knowledge to divide frequency bands and compresses frequency information. We employ a multi-scale selective attention module to extract contextual features while introducing a full-frequency-frame attention module to capture both temporal and frequency contextual information. Additionally, to more realistically evaluate the performance of speech separation models in complex acoustic environments, we introduce a dataset called EchoSet. This dataset includes noise and more realistic reverberation (e.g., considering object occlusions and material properties), with speech from two speakers overlapping at random proportions. Experimental results showed that models trained on EchoSet had better generalization ability than those trained on other datasets compared to the data collected in the physical world, which validated the practical value of the EchoSet. On EchoSet and real-world data, TIGER significantly reduces the number of parameters by 94.3% and the MACs by 95.3% while achieving performance surpassing the state-of-the-art (SOTA) model TF-GridNet.", "date": "2024-10-02", "categories": []}
{"id": "2602.22431", "title": "mmWave Radar Aware Dual-Conditioned GAN for Speech Reconstruction of Signals With Low SNR", "abstract": "Millimeter-wave (mmWave) radar captures are band-limited and noisy, making for difficult reconstruction of intelligible full-bandwidth speech. In this work, we propose a two-stage speech reconstruction pipeline for mmWave using a Radar-Aware Dual-conditioned Generative Adversarial Network (RAD-GAN), which is capable of performing bandwidth extension on signals with low signal-to-noise ratios (-5 dB to -1 dB), captured through glass walls. We propose an mmWave-tailored Multi-Mel Discriminator (MMD) and a Residual Fusion Gate (RFG) to enhance the generator input to process multiple conditioning channels. The proposed two-stage pipeline involves pretraining the model on synthetically clipped clean speech and finetuning on fused mel spectrograms generated by the RFG. We empirically show that the proposed method, trained on a limited dataset, with no pre-trained modules, and no data augmentations, outperformed state-of-the-art approaches for this specific task. Audio examples of RAD-GAN are available online at https://rad-gan-demo-site.vercel.app/.", "date": "2026-02-25", "categories": []}
{"id": "2602.21476", "title": "A Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation", "abstract": "We propose a knowledge-driven, model-based approach to segmenting audio into single-category and mixed-category chunks with applications to source separation. \"Knowledge\" here denotes information associated with the data, such as music scores. \"Model\" here refers to tool that can be used for audio segmentation and recognition, such as hidden Markov models. In contrast to conventional learning that often relies on annotated data with given segment categories and their corresponding boundaries to guide the learning process, the proposed framework does not depend on any pre-segmented training data and learns directly from the input audio and its related knowledge sources to build all necessary models autonomously. Evaluation on simulation data shows that score-guided learning achieves very good music segmentation and separation results. Tested on movie track data for cinematic audio source separation also shows that utilizing sound category knowledge achieves better separation results than those obtained with data-driven techniques without using such information.", "date": "2026-02-25", "categories": []}
{"id": "2507.03854", "title": "Latent FxLMS: Accelerating Active Noise Control with Neural Adaptive Filters", "abstract": "Filtered-X LMS (FxLMS) is commonly used for active noise control (ANC), wherein the soundfield is minimized at a desired location. Given prior knowledge of the spatial region of the noise or control sources, we could improve FxLMS by adapting along the low-dimensional manifold of possible adaptive filter weights. We train an auto-encoder on the filter coefficients of the steady-state adaptive filter for each primary source location sampled from a given spatial region and constrain the weights of the adaptive filter to be the output of the decoder for a given state of latent variables. Then, we perform updates in the latent space and use the decoder to generate the cancellation filter. We evaluate how various neural network constraints and normalization techniques impact the convergence speed and steady-state mean squared error. Under certain conditions, our Latent FxLMS model converges in fewer steps with comparable steady-state error to the standard FxLMS.", "date": "2025-07-05", "categories": []}
{"id": "2602.15082", "title": "S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization", "abstract": "Neural audio compression models have recently achieved extreme compression rates, enabling efficient latent generative modeling. Conversely, latent generative models have been applied to compression, pushing the limits of continuous and discrete approaches. However, existing methods remain constrained to low-resolution audio and degrade substantially at very low bitrates, where audible artifacts are prominent. In this paper, we present S-PRESSO, a 48kHz sound effect compression model that produces both continuous and discrete embeddings at ultra-low bitrates, down to 0.096 kbps, via offline quantization. Our model relies on a pretrained latent diffusion model to decode compressed audio embeddings learned by a latent encoder. Leveraging the generative priors of the diffusion decoder, we achieve extremely low frame rates, down to 1Hz (750x compression rate), producing convincing and realistic reconstructions at the cost of exact fidelity. Despite operating at high compression rates, we demonstrate that S-PRESSO outperforms both continuous and discrete baselines in audio quality, acoustic similarity and reconstruction metrics.", "date": "2026-02-16", "categories": []}
{"id": "2308.00010", "title": "Monaural Multi-Speaker Speech Separation Using Efficient Transformer Model", "abstract": "Cocktail party problem is the scenario where it is difficult to separate or distinguish individual speaker from a mixed speech from several speakers. There have been several researches going on in this field but the size and complexity of the model is being traded off with the accuracy and robustness of speech separation. \"Monaural multi-speaker speech separation\" presents a speech-separation model based on the Transformer architecture and its efficient forms. The model has been trained with the LibriMix dataset containing diverse speakers' utterances. The model separates 2 distinct speaker sources from a mixed audio input. The developed model approaches the reduction in computational complexity of the speech separation model, with minimum tradeoff with the performance of prevalent speech separation model and it has shown significant movement towards that goal. This project foresees, a rise in contribution towards the ongoing research in the field of speech separation with computational efficiency at its core.", "date": "2023-07-29", "categories": []}
{"id": "2507.08333", "title": "Token-Based Audio Inpainting via Discrete Diffusion", "abstract": "Audio inpainting seeks to restore missing segments in degraded recordings. Previous diffusion-based methods exhibit impaired performance when the missing region is large. We introduce the first approach that applies discrete diffusion over tokenized music representations from a pre-trained audio tokenizer, enabling stable and semantically coherent restoration of long gaps. Our method further incorporates two training approaches: a derivative-based regularization loss that enforces smooth temporal dynamics, and a span-based absorbing transition that provides structured corruption during diffusion. Experiments on the MusicNet and MAESTRO datasets with gaps up to 750 ms show that our approach consistently outperforms strong baselines across range of gap lengths, for gaps of 150 ms and above. This work advances musical audio restoration and introduces new directions for discrete diffusion model training. Visit our project page for examples and code.", "date": "2025-07-11", "categories": []}
{"id": "2510.10509", "title": "MARS-Sep: Multimodal-Aligned Reinforced Sound Separation", "abstract": "Universal sound separation faces a fundamental misalignment: models optimized for low-level signal metrics often produce semantically contaminated outputs, failing to suppress perceptually salient interference from acoustically similar sources. We introduce a preference alignment perspective, analogous to aligning LLMs with human intent. To address this, we introduce MARS-Sep, a reinforcement learning framework that reformulates separation as decision making. Instead of simply regressing ground-truth masks, MARS-Sep learns a factorized Beta mask policy that is steered by a preference reward model and optimized by a stable, clipped trust-region surrogate. The reward, derived from a progressively-aligned audio-text-vision encoder, directly incentivizes semantic consistency with query prompts. Extensive experiments on multiple benchmarks demonstrate consistent gains in Text-, Audio-, and Image-Queried separation, with notable improvements in signal metrics and semantic quality. Our code is available at https://github.com/mars-sep/MARS-Sep. Sound separation samples are available at https://mars-sep.github.io/.", "date": "2025-10-12", "categories": []}
{"id": "2602.15491", "title": "The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs", "abstract": "Neural audio codecs (NACs) typically encode the short-term energy (gain) and normalized structure (shape) of speech/audio signals jointly within the same latent space. As a result, they are poorly robust to a global variation of the input signal level in the sense that such variation has strong influence on the embedding vectors at the output of the encoder and their quantization. This methodology is inherently inefficient, leading to codebook redundancy and suboptimal bitrate-distortion performance. To address these limitations, we propose to introduce shape-gain decomposition, widely used in classical speech/audio coding, into the NAC framework. The principle of the proposed Equalizer methodology is to decompose the input signal -- before the NAC encoder -- into gain and normalized shape vector on a short-term basis. The shape vector is processed by the NAC, while the gain is quantized with scalar quantization and transmitted separately. The output (decoded) signal is reconstructed from the normalized output of the NAC and the quantized gain. Our experiments conducted on speech signals show that this general methodology, easily applicable to any NAC, enables a substantial gain in bitrate-distortion performance, as well as a massive reduction in complexity.", "date": "2026-02-17", "categories": []}
{"id": "2504.10793", "title": "SonicSieve: Bringing Directional Speech Extraction to Smartphones Using Acoustic Microstructures", "abstract": "Imagine placing your smartphone on a table in a noisy restaurant and clearly capturing the voices of friends seated around you, or recording a lecturer's voice with clarity in a reverberant auditorium. We introduce SonicSieve, the first intelligent directional speech extraction system for smartphones using a bio-inspired acoustic microstructure. Our passive design embeds directional cues onto incoming speech without any additional electronics. It attaches to the in-line mic of low-cost wired earphones which can be attached to smartphones. We present an end-to-end neural network that processes the raw audio mixtures in real-time on mobile devices. Our results show that SonicSieve achieves a signal quality improvement of 5.0 dB when focusing on a 30{\\deg} angular region. Additionally, the performance of our system based on only two microphones exceeds that of conventional 5-microphone arrays.", "date": "2025-04-15", "categories": []}
{"id": "2602.11145", "title": "SCRAPL: Scattering Transform with Random Paths for Machine Learning", "abstract": "The Euclidean distance between wavelet scattering transform coefficients (known as paths) provides informative gradients for perceptual quality assessment of deep inverse problems in computer vision, speech, and audio processing. However, these transforms are computationally expensive when employed as differentiable loss functions for stochastic gradient descent due to their numerous paths, which significantly limits their use in neural network training. Against this problem, we propose \"Scattering transform with Random Paths for machine Learning\" (SCRAPL): a stochastic optimization scheme for efficient evaluation of multivariable scattering transforms. We implement SCRAPL for the joint time-frequency scattering transform (JTFS) which demodulates spectrotemporal patterns at multiple scales and rates, allowing a fine characterization of intermittent auditory textures. We apply SCRAPL to differentiable digital signal processing (DDSP), specifically, unsupervised sound matching of a granular synthesizer and the Roland TR-808 drum machine. We also propose an initialization heuristic based on importance sampling, which adapts SCRAPL to the perceptual content of the dataset, improving neural network convergence and evaluation performance. We make our code and audio samples available and provide SCRAPL as a Python package.", "date": "2026-02-11", "categories": []}
{"id": "2602.06917", "title": "Automatic Detection and Analysis of Singing Mistakes for Music Pedagogy", "abstract": "The advancement of machine learning in audio analysis has opened new possibilities for technology-enhanced music education. This paper introduces a framework for automatic singing mistake detection in the context of music pedagogy, supported by a newly curated dataset. The dataset comprises synchronized teacher learner vocal recordings, with annotations marking different types of mistakes made by learners. Using this dataset, we develop different deep learning models for mistake detection and benchmark them. To compare the efficacy of mistake detection systems, a new evaluation methodology is proposed. Experiments indicate that the proposed learning-based methods are superior to rule-based methods. A systematic study of errors and a cross-teacher study reveal insights into music pedagogy that can be utilised for various music applications. This work sets out new directions of research in music pedagogy. The codes and dataset are publicly available.", "date": "2026-02-06", "categories": []}
{"id": "2602.06937", "title": "Reciprocal Latent Fields for Precomputed Sound Propagation", "abstract": "Realistic sound propagation is essential for immersion in a virtual scene, yet physically accurate wave-based simulations remain computationally prohibitive for real-time applications. Wave coding methods address this limitation by precomputing and compressing impulse responses of a given scene into a set of scalar acoustic parameters, which can reach unmanageable sizes in large environments with many source-receiver pairs. We introduce Reciprocal Latent Fields (RLF), a memory-efficient framework for encoding and predicting these acoustic parameters. The RLF framework employs a volumetric grid of trainable latent embeddings decoded with a symmetric function, ensuring acoustic reciprocity. We study a variety of decoders and show that leveraging Riemannian metric learning leads to a better reproduction of acoustic phenomena in complex scenes. Experimental validation demonstrates that RLF maintains replication quality while reducing the memory footprint by several orders of magnitude. Furthermore, a MUSHRA-like subjective listening test indicates that sound rendered via RLF is perceptually indistinguishable from ground-truth simulations.", "date": "2026-02-06", "categories": []}
{"id": "2510.00771", "title": "UniverSR: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching", "abstract": "In this paper, we present a vocoder-free framework for audio super-resolution that employs a flow matching generative model to capture the conditional distribution of complex-valued spectral coefficients. Unlike conventional two-stage diffusion-based approaches that predict a mel-spectrogram and then rely on a pre-trained neural vocoder to synthesize waveforms, our method directly reconstructs waveforms via the inverse Short-Time Fourier Transform (iSTFT), thereby eliminating the dependence on a separate vocoder. This design not only simplifies end-to-end optimization but also overcomes a critical bottleneck of two-stage pipelines, where the final audio quality is fundamentally constrained by vocoder performance. Experiments show that our model consistently produces high-fidelity 48 kHz audio across diverse upsampling factors, achieving state-of-the-art performance on both speech and general audio datasets.", "date": "2025-10-01", "categories": []}
{"id": "2506.00934", "title": "GRAM: Spatial general-purpose audio representation models for real-world applications", "abstract": "Audio foundation models learn general-purpose audio representations that facilitate a wide range of downstream tasks. While the performance of these models has greatly increased for conventional single-channel, dry audio clips, their success in real-world acoustic environments with reverberation and noise is limited. Furthermore, most audio foundation models ignore the spatial dimension of real-world acoustic environments, ruling out tasks involving sound localization. To address these limitations, we propose GRAM: a general-purpose real-world audio model that employs a multi-channel masked autoencoder to efficiently learn spatial audio representations. We evaluated GRAM and other audio foundation models in a standardized manner on high-quality simulations of naturalistic, spatial acoustic environments as well as recordings of real-world environments and release these two complementary benchmark task suites: NatHEAR and RealSELD. Our results demonstrate that GRAM outperforms all state-of-the-art self-supervised audio foundation models on NatHEAR and the clean, single-channel version HEAR, while using only a fraction of the training data. GRAM also shows state-of-the-art localization performance in simulated environments and generalizes efficiently to real-world recordings in RealSELD. Taken together, GRAM presents a significant advance toward robust spatial audio foundation models for real-world environments.", "date": "2025-06-01", "categories": []}
{"id": "2601.17517", "title": "EuleroDec: A Complex-Valued RVQ-VAE for Efficient and Robust Audio Coding", "abstract": "Audio codecs power discrete music generative modelling, music streaming and immersive media by shrinking PCM audio to bandwidth-friendly bit-rates. Recent works have gravitated towards processing in the spectral domain; however, spectrogram-domains typically struggle with phase modeling which is naturally complex-valued. Most frequency-domain neural codecs either disregard phase information or encode it as two separate real-valued channels, limiting spatial fidelity. This entails the need to introduce adversarial discriminators at the expense of convergence speed and training stability to compensate for the inadequate representation power of the audio signal. In this work we introduce an end-to-end complex-valued RVQ-VAE audio codec that preserves magnitude-phase coupling across the entire analysis-quantization-synthesis pipeline and removes adversarial discriminators and diffusion post-filters. Without GANs or diffusion we match or surpass much longer-trained baselines in-domain and reach SOTA out-of-domain performance. Compared to standard baselines that train for hundreds of thousands of steps, our model reducing training budget by an order of magnitude is markedly more compute-efficient while preserving high perceptual quality.", "date": "2026-01-24", "categories": []}
{"id": "2509.18561", "title": "SoundCompass: Navigating Target Sound Extraction With Effective Directional Clue Integration In Complex Acoustic Scenes", "abstract": "Recent advances in target sound extraction (TSE) utilize directional clues derived from direction of arrival (DoA), which represent an inherent spatial property of sound available in any acoustic scene. However, previous DoA-based methods rely on hand-crafted features or discrete encodings, which lose fine-grained spatial information and limit adaptability. We propose SoundCompass, an effective directional clue integration framework centered on a Spectral Pairwise INteraction (SPIN) module that captures cross-channel spatial correlations in the complex spectrogram domain to preserve full spatial information in multichannel signals. The input feature expressed in terms of spatial correlations is fused with a DoA clue represented as spherical harmonics (SH) encoding. The fusion is carried out across overlapping frequency subbands, inheriting the benefits reported in the previous band-split architectures. We also incorporate the iterative refinement strategy, chain-of-inference (CoI), in the TSE framework, which recursively fuses DoA with sound event activation estimated from the previous inference stage. Experiments demonstrate that SoundCompass, combining SPIN, SH embedding, and CoI, robustly extracts target sources across diverse signal classes and spatial configurations.", "date": "2025-09-23", "categories": []}
{"id": "2510.10718", "title": "HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing", "abstract": "Direction of Arrival (DoA) estimation techniques face a critical trade-off, as classical methods often lack accuracy in challenging, low signal-to-noise ratio (SNR) conditions, while modern deep learning approaches are too energy-intensive and opaque for resource-constrained, safety-critical systems. We introduce HYPERDOA, a novel estimator leveraging Hyperdimensional Computing (HDC). The framework introduces two distinct feature extraction strategies -- Mean Spatial-Lag Autocorrelation and Spatial Smoothing -- for its HDC pipeline, and then reframes DoA estimation as a pattern recognition problem. This approach leverages HDC's inherent robustness to noise and its transparent algebraic operations to bypass the expensive matrix decompositions and \"black-box\" nature of classical and deep learning methods, respectively. Our evaluation demonstrates that HYPERDOA achieves ~35.39% higher accuracy than state-of-the-art methods in low-SNR, coherent-source scenarios. Crucially, it also consumes ~93% less energy than competing neural baselines on an embedded NVIDIA Jetson Xavier NX platform. This dual advantage in accuracy and efficiency establishes HYPERDOA as a robust and viable solution for mission-critical applications on edge devices.", "date": "2025-10-12", "categories": []}
{"id": "2601.19533", "title": "SLM-SS: Speech Language Model for Generative Speech Separation", "abstract": "Speech separation (SS) has advanced significantly with neural network-based methods, showing improved performance on signal-level metrics. However, these methods often struggle to maintain speech intelligibility in the separated signals, which can negatively affect the performance of downstream tasks such as speech recognition. In this work, we propose SLM-SS, a novel approach that applies speech language models to SS, aiming to enhance the intelligibility and coherence of the separated signals. We frame SS as discrete multi-codebook sequence generation, using Encoder-Decoder models to map quantized speech mixtures to target tokens. In addition to the autoregressive modeling strategy, we introduce a non-autoregressive model to improve decoding efficiency for residual tokens. Experimental results on the LibriMix dataset demonstrate that our approach shows significantly better preservation of speech intelligibility, leading to improved linguistic consistency in a variety of downstream tasks compared to existing approaches.", "date": "2026-01-27", "categories": []}
{"id": "2601.19702", "title": "SAM Audio Judge: A Unified Multimodal Framework for Perceptual Evaluation of Audio Separation", "abstract": "The performance evaluation remains a complex challenge in audio separation, and existing evaluation metrics are often misaligned with human perception, course-grained, relying on ground truth signals. On the other hand, subjective listening tests remain the gold standard for real-world evaluation, but they are expensive, time-consuming, and difficult to scale. This paper addresses the growing need for automated systems capable of evaluating audio separation without human intervention. The proposed evaluation metric, SAM Audio Judge (SAJ), is a multimodal fine-grained reference-free objective metric, which shows highly alignment with human perceptions. SAJ supports three audio domains (speech, music and general sound events) and three prompt inputs (text, visual and span), covering four different dimensions of evaluation (recall, percision, faithfulness, and overall). SAM Audio Judge also shows potential applications in data filtering, pseudo-labeling large datasets and reranking in audio separation models. We release our code and pre-trained models at: https://github.com/facebookresearch/sam-audio.", "date": "2026-01-27", "categories": []}
{"id": "2403.20130", "title": "Sound event localization and classification using WASN in Outdoor Environment", "abstract": "Deep learning-based sound event localization and classification is an emerging research area within wireless acoustic sensor networks. However, current methods for sound event localization and classification typically rely on a single microphone array, making them susceptible to signal attenuation and environmental noise, which limits their monitoring range. Moreover, methods using multiple microphone arrays often focus solely on source localization, neglecting the aspect of sound event classification. In this paper, we propose a deep learning-based method that employs multiple features and attention mechanisms to estimate the location and class of sound source. We introduce a Soundmap feature to capture spatial information across multiple frequency bands. We also use the Gammatone filter to generate acoustic features more suitable for outdoor environments. Furthermore, we integrate attention mechanisms to learn channel-wise relationships and temporal dependencies within the acoustic features. To evaluate our proposed method, we conduct experiments using simulated datasets with different levels of noise and size of monitoring areas, as well as different arrays and source positions. The experimental results demonstrate the superiority of our proposed method over state-of-the-art methods in both sound event classification and sound source localization tasks. And we provide further analysis to explain the reasons for the observed errors.", "date": "2024-03-29", "categories": []}
{"id": "2601.17611", "title": "ToS: A Team of Specialists ensemble framework for Stereo Sound Event Localization and Detection with distance estimation in Video", "abstract": "Sound event localization and detection with distance estimation (3D SELD) in video involves identifying active sound events at each time frame while estimating their spatial coordinates. This multimodal task requires joint reasoning across semantic, spatial, and temporal dimensions, a challenge that single models often struggle to address effectively. To tackle this, we introduce the Team of Specialists (ToS) ensemble framework, which integrates three complementary sub-networks: a spatio-linguistic model, a spatio-temporal model, and a tempo-linguistic model. Each sub-network specializes in a unique pair of dimensions, contributing distinct insights to the final prediction, akin to a collaborative team with diverse expertise. ToS has been benchmarked against state-of-the-art audio-visual models for 3D SELD on the DCASE2025 Task 3 Stereo SELD development set, consistently outperforming existing methods across key metrics. Future work will extend this proof of concept by strengthening the specialists with appropriate tasks, training, and pre-training curricula.", "date": "2026-01-24", "categories": []}
{"id": "2601.17690", "title": "Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance", "abstract": "Audio fingerprinting provides an identifiable representation of acoustic signals, which can be later used for identification and retrieval systems. To obtain a discriminative representation, the input audio is usually segmented into shorter time intervals, allowing local acoustic features to be extracted and analyzed. Modern neural approaches typically operate on short, fixed-duration audio segments, yet the choice of segment duration is often made heuristically and rarely examined in depth. In this paper, we study how segment length affects audio fingerprinting performance. We extend an existing neural fingerprinting architecture to adopt various segment lengths and evaluate retrieval accuracy across different segment lengths and query durations. Our results show that short segment lengths (0.5-second) generally achieve better performance. Moreover, we evaluate LLM capacity in recommending the best segment length, which shows that GPT-5-mini consistently gives the best suggestions across five considerations among three studied LLMs. Our findings provide practical guidance for selecting segment duration in large-scale neural audio retrieval systems.", "date": "2026-01-25", "categories": []}
{"id": "2601.18335", "title": "Analytic Incremental Learning For Sound Source Localization With Imbalance Rectification", "abstract": "Sound source localization (SSL) demonstrates remarkable results in controlled settings but struggles in real-world deployment due to dual imbalance challenges: intra-task imbalance arising from long-tailed direction-of-arrival (DoA) distributions, and inter-task imbalance induced by cross-task skews and overlaps. These often lead to catastrophic forgetting, significantly degrading the localization accuracy. To mitigate these issues, we propose a unified framework with two key innovations. Specifically, we design a GCC-PHAT-based data augmentation (GDA) method that leverages peak characteristics to alleviate intra-task distribution skews. We also propose an Analytic dynamic imbalance rectifier (ADIR) with task-adaption regularization, which enables analytic updates that adapt to inter-task dynamics. On the SSLR benchmark, our proposal achieves state-of-the-art (SoTA) results of 89.0% accuracy, 5.3{\\deg} mean absolute error, and 1.6 backward transfer, demonstrating robustness to evolving imbalances without exemplar storage.", "date": "2026-01-26", "categories": []}
{"id": "2504.17586", "title": "A Machine Learning Approach for Denoising and Upsampling HRTFs", "abstract": "The demand for realistic virtual immersive audio continues to grow, with Head-Related Transfer Functions (HRTFs) playing a key role. HRTFs capture how sound reaches our ears, reflecting unique anatomical features and enhancing spatial perception. It has been shown that personalized HRTFs improve localization accuracy, but their measurement remains time-consuming and requires a noise-free environment. Although machine learning has been shown to reduce the required measurement points and, thus, the measurement time, a controlled environment is still necessary. This paper proposes a method to address this constraint by presenting a novel technique that can upsample sparse, noisy HRTF measurements. The proposed approach combines an HRTF Denoisy U-Net for denoising and an Autoencoding Generative Adversarial Network (AE-GAN) for upsampling from three measurement points. The proposed method achieves a log-spectral distortion (LSD) error of 5.41 dB and a cosine similarity loss of 0.0070, demonstrating the method's effectiveness in HRTF upsampling.", "date": "2025-04-24", "categories": []}
{"id": "2509.16715", "title": "QASTAnet: A DNN-based Quality Metric for Spatial Audio", "abstract": "In the development of spatial audio technologies, reliable and shared methods for evaluating audio quality are essential. Listening tests are currently the standard but remain costly in terms of time and resources. Several models predicting subjective scores have been proposed, but they do not generalize well to real-world signals. In this paper, we propose QASTAnet (Quality Assessment for SpaTial Audio network), a new metric based on a deep neural network, specialized on spatial audio (ambisonics and binaural). As training data is scarce, we aim for the model to be trainable with a small amount of data. To do so, we propose to rely on expert modeling of the low-level auditory system and use a neurnal network to model the high-level cognitive function of the quality judgement. We compare its performance to two reference metrics on a wide range of content types (speech, music, ambiance, anechoic, reverberated) and focusing on codec artifacts. Results demonstrate that QASTAnet overcomes the aforementioned limitations of the existing methods. The strong correlation between the proposed metric prediction and subjective scores makes it a good candidate for comparing codecs in their development.", "date": "2025-09-20", "categories": []}
{"id": "2601.11768", "title": "Lightweight Self-Supervised Detection of Fundamental Frequency and Accurate Probability of Voicing in Monophonic Music", "abstract": "Reliable fundamental frequency (F 0) and voicing estimation is essential for neural synthesis, yet many pitch extractors depend on large labeled corpora and degrade under realistic recording artifacts. We propose a lightweight, fully self-supervised framework for joint F 0 estimation and voicing inference, designed for rapid single-instrument training from limited audio. Using transposition-equivariant learning on CQT features, we introduce an EM-style iterative reweighting scheme that uses Shift Cross-Entropy (SCE) consistency as a reliability signal to suppress uninformative noisy/unvoiced frames. The resulting weights provide confidence scores that enable pseudo-labeling for a separate lightweight voicing classifier without manual annotations. Trained on MedleyDB and evaluated on MDB-stem-synth ground truth, our method achieves competitive cross-corpus performance (RPA 95.84, RCA 96.24) and demonstrates cross-instrument generalization.", "date": "2026-01-16", "categories": []}
{"id": "2601.12205", "title": "Do Neural Codecs Generalize? A Controlled Study Across Unseen Languages and Non-Speech Tasks", "abstract": "This paper investigates three crucial yet underexplored aspects of the generalization capabilities of neural audio codecs (NACs): (i) whether NACs can generalize to unseen languages during pre-training, (ii) whether speech-only pre-trained NACs can effectively generalize to non-speech applications such as environmental sounds, music, and animal vocalizations, and (iii) whether incorporating non-speech data during pre-training can improve performance on both speech and non-speech tasks. Existing studies typically rely on off-the-shelf NACs for comparison, which limits insight due to variations in implementation. In this work, we train NACs from scratch using strictly controlled configurations and carefully curated pre-training data to enable fair comparisons. We conduct a comprehensive evaluation of NAC performance on both signal reconstruction quality and downstream applications using 11 metrics. Our results show that NACs can generalize to unseen languages during pre-training, speech-only pre-trained NACs exhibit degraded performance on non-speech tasks, and incorporating non-speech data during pre-training improves performance on non-speech tasks while maintaining comparable performance on speech tasks.", "date": "2026-01-18", "categories": []}
{"id": "2601.12354", "title": "Bone-conduction Guided Multimodal Speech Enhancement with Conditional Diffusion Models", "abstract": "Single-channel speech enhancement models face significant performance degradation in extremely noisy environments. While prior work has shown that complementary bone-conducted speech can guide enhancement, effective integration of this noise-immune modality remains a challenge. This paper introduces a novel multimodal speech enhancement framework that integrates bone-conduction sensors with air-conducted microphones using a conditional diffusion model. Our proposed model significantly outperforms previously established multimodal techniques and a powerful diffusion-based single-modal baseline across a wide range of acoustic conditions.", "date": "2026-01-18", "categories": []}
{"id": "2601.12752", "title": "SoundPlot: An Open-Source Framework for Birdsong Acoustic Analysis and Neural Synthesis with Interactive 3D Visualization", "abstract": "We present SoundPlot, an open-source framework for analyzing avian vocalizations through acoustic feature extraction, dimensionality reduction, and neural audio synthesis. The system transforms audio signals into a multi-dimensional acoustic feature space, enabling real-time visualization of temporal dynamics in 3D using web-based interactive graphics. Our framework implements a complete analysis-synthesis pipeline that extracts spectral features (centroid, bandwidth, contrast), pitch contours via probabilistic YIN (pYIN), and mel-frequency cepstral coefficients (MFCCs), mapping them to a unified timbre space for visualization. Audio reconstruction employs the Griffin-Lim phase estimation algorithm applied to mel spectrograms. The accompanying Three.js-based interface provides dual-viewport visualization comparing original and synthesized audio trajectories with independent playback controls. We demonstrate the framework's capabilities through comprehensive waveform analysis, spectrogram comparisons, and feature space evaluation using Principal Component Analysis (PCA). Quantitative evaluation shows mel spectrogram correlation scores exceeding 0.92, indicating high-fidelity preservation of perceptual acoustic structure. SoundPlot is released under the MIT License to facilitate research in bioacoustics, audio signal processing, and computational ethology.", "date": "2026-01-19", "categories": []}
{"id": "2601.13849", "title": "Co-Initialization of Control Filter and Secondary Path via Meta-Learning for Active Noise Control", "abstract": "Active noise control (ANC) must adapt quickly when the acoustic environment changes, yet early performance is largely dictated by initialization. We address this with a Model-Agnostic Meta-Learning (MAML) co-initialization that jointly sets the control filter and the secondary-path model for FxLMS-based ANC while keeping the runtime algorithm unchanged. The initializer is pre-trained on a small set of measured paths using short two-phase inner loops that mimic identification followed by residual-noise reduction, and is applied by simply setting the learned initial coefficients. In an online secondary path modeling FxLMS testbed, it yields lower early-stage error, shorter time-to-target, reduced auxiliary-noise energy, and faster recovery after path changes than a baseline without re-initialization. The method provides a simple fast start for feedforward ANC under environment changes, requiring a small set of paths to pre-train.", "date": "2026-01-20", "categories": []}
{"id": "2601.04343", "title": "Summary of The Inaugural Music Source Restoration Challenge", "abstract": "Music Source Restoration (MSR) aims to recover original, unprocessed instrument stems from professionally mixed and degraded audio, requiring the reversal of both production effects and real-world degradations. We present the inaugural MSR Challenge, which features objective evaluation on studio-produced mixtures using Multi-Mel-SNR, Zimtohrli, and FAD-CLAP, alongside subjective evaluation on real-world degraded recordings. Five teams participated in the challenge. The winning system achieved 4.46 dB Multi-Mel-SNR and 3.47 MOS-Overall, corresponding to relative improvements of 91% and 18% over the second-place system, respectively. Per-stem analysis reveals substantial variation in restoration difficulty across instruments, with bass averaging 4.59 dB across all teams, while percussion averages only 0.29 dB. The dataset, evaluation protocols, and baselines are available at https://msrchallenge.com/.", "date": "2026-01-07", "categories": []}
{"id": "2601.03443", "title": "Discriminating real and synthetic super-resolved audio samples using embedding-based classifiers", "abstract": "Generative adversarial networks (GANs) and diffusion models have recently achieved state-of-the-art performance in audio super-resolution (ADSR), producing perceptually convincing wideband audio from narrowband inputs. However, existing evaluations primarily rely on signal-level or perceptual metrics, leaving open the question of how closely the distributions of synthetic super-resolved and real wideband audio match. Here we address this problem by analyzing the separability of real and super-resolved audio in various embedding spaces. We consider both middle-band ($4\\to 16$~kHz) and full-band ($16\\to 48$~kHz) upsampling tasks for speech and music, training linear classifiers to distinguish real from synthetic samples based on multiple types of audio embeddings. Comparisons with objective metrics and subjective listening tests reveal that embedding-based classifiers achieve near-perfect separation, even when the generated audio attains high perceptual quality and state-of-the-art metric scores. This behavior is consistent across datasets and models, including recent diffusion-based approaches, highlighting a persistent gap between perceptual quality and true distributional fidelity in ADSR models.", "date": "2026-01-06", "categories": []}
{"id": "2601.02776", "title": "UniSRCodec: Unified and Low-Bitrate Single Codebook Codec with Sub-Band Reconstruction", "abstract": "Neural Audio Codecs (NACs) can reduce transmission overhead by performing compact compression and reconstruction, which also aim to bridge the gap between continuous and discrete signals. Existing NACs can be divided into two categories: multi-codebook and single-codebook codecs. Multi-codebook codecs face challenges such as structural complexity and difficulty in adapting to downstream tasks, while single-codebook codecs, though structurally simpler, suffer from low-fidelity, ineffective modeling of unified audio, and an inability to support modeling of high-frequency audio. We propose the UniSRCodec, a single-codebook codec capable of supporting high sampling rate, low-bandwidth, high fidelity, and unified. We analyze the inefficiency of waveform-based compression and introduce the time and frequency compression method using the Mel-spectrogram, and cooperate with a Vocoder to recover the phase information of the original audio. Moreover, we propose a sub-band reconstruction technique to achieve high-quality compression across both low and high frequency bands. Subjective and objective experimental results demonstrate that UniSRCodec achieves state-of-the-art (SOTA) performance among cross-domain single-codebook codecs with only a token rate of 40, and its reconstruction quality is comparable to that of certain multi-codebook methods. Our demo page is available at https://wxzyd123.github.io/unisrcodec.", "date": "2026-01-06", "categories": []}
{"id": "2509.05175", "title": "Room-acoustic simulations as an alternative to measurements for audio-algorithm evaluation", "abstract": "Audio-signal-processing and audio-machine-learning (ASP/AML) algorithms are ubiquitous in modern technology like smart devices, wearables, and entertainment systems. Development of such algorithms and models typically involves a formal evaluation to demonstrate their effectiveness and progress beyond the state-of-the-art. Ideally, a thorough evaluation should cover many diverse application scenarios and room-acoustic conditions. However, in practice, evaluation datasets are often limited in size and diversity because they rely on costly and time-consuming measurements. This paper explores how room-acoustic simulations can be used for evaluating ASP/AML algorithms. To this end, we evaluate three ASP/AML algorithms with room-acoustic measurements and data from different simulation engines, and assess the match between the evaluation results obtained from measurements and simulations. The presented investigation compares a numerical wave-based solver with two geometrical acoustics simulators. While numerical wave-based simulations yielded similar evaluation results as measurements for all three evaluated ASP/AML algorithms, geometrical acoustic simulations could not replicate the measured evaluation results as reliably.", "date": "2025-09-05", "categories": []}
{"id": "2508.14556", "title": "Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions", "abstract": "We introduce a new music source separation model tailored for accurate vocal isolation. Unlike Transformer-based approaches, which often fail to capture intermittently occurring vocals, our model leverages Mamba2, a recent state space model, to better capture long-range temporal dependencies. To handle long input sequences efficiently, we combine a band-splitting strategy with a dual-path architecture. Experiments show that our approach outperforms recent state-of-the-art models, achieving a cSDR of 11.03 dB-the best reported to date-and delivering substantial gains in uSDR. Moreover, the model exhibits stable and consistent performance across varying input lengths and vocal occurrence patterns. These results demonstrate the effectiveness of Mamba-based models for high-resolution audio processing and open up new directions for broader applications in audio research.", "date": "2025-08-20", "categories": []}
{"id": "2509.17609", "title": "Audio Super-Resolution with Latent Bridge Models", "abstract": "Audio super-resolution (SR), i.e., upsampling the low-resolution (LR) waveform to the high-resolution (HR) version, has recently been explored with diffusion and bridge models, while previous methods often suffer from sub-optimal upsampling quality due to their uninformative generation prior. Towards high-quality audio super-resolution, we present a new system with latent bridge models (LBMs), where we compress the audio waveform into a continuous latent space and design an LBM to enable a latent-to-latent generation process that naturally matches the LR-toHR upsampling process, thereby fully exploiting the instructive prior information contained in the LR waveform. To further enhance the training results despite the limited availability of HR samples, we introduce frequency-aware LBMs, where the prior and target frequency are taken as model input, enabling LBMs to explicitly learn an any-to-any upsampling process at the training stage. Furthermore, we design cascaded LBMs and present two prior augmentation strategies, where we make the first attempt to unlock the audio upsampling beyond 48 kHz and empower a seamless cascaded SR process, providing higher flexibility for audio post-production. Comprehensive experimental results evaluated on the VCTK, ESC-50, Song-Describer benchmark datasets and two internal testsets demonstrate that we achieve state-of-the-art objective and perceptual quality for any-to-48kHz SR across speech, audio, and music signals, as well as setting the first record for any-to-192kHz audio SR. Demo at https://AudioLBM.github.io/.", "date": "2025-09-22", "categories": []}
{"id": "2512.18099", "title": "SAM Audio: Segment Anything in Audio", "abstract": "General audio source separation is a key capability for multimodal AI systems that can perceive and reason about sound. Despite substantial progress in recent years, existing separation models are either domain-specific, designed for fixed categories such as speech or music, or limited in controllability, supporting only a single prompting modality such as text. In this work, we present SAM Audio, a foundation model for general audio separation that unifies text, visual, and temporal span prompting within a single framework. Built on a diffusion transformer architecture, SAM Audio is trained with flow matching on large-scale audio data spanning speech, music, and general sounds, and can flexibly separate target sources described by language, visual masks, or temporal spans. The model achieves state-of-the-art performance across a diverse suite of benchmarks, including general sound, speech, music, and musical instrument separation in both in-the-wild and professionally produced audios, substantially outperforming prior general-purpose and specialized systems. Furthermore, we introduce a new real-world separation benchmark with human-labeled multimodal prompts and a reference-free evaluation model that correlates strongly with human judgment.", "date": "2025-12-19", "categories": []}
{"id": "2512.15532", "title": "A Conditioned UNet for Music Source Separation", "abstract": "In this paper we propose a conditioned UNet for Music Source Separation (MSS). MSS is generally performed by multi-output neural networks, typically UNets, with each output representing a particular stem from a predefined instrument vocabulary. In contrast, conditioned MSS networks accept an audio query related to a stem of interest alongside the signal from which that stem is to be extracted. Thus, a strict vocabulary is not required and this enables more realistic tasks in MSS. The potential of conditioned approaches for such tasks has been somewhat hidden due to a lack of suitable data, an issue recently addressed with the MoisesDb dataset. A recent method, Banquet, employs this dataset with promising results seen on larger vocabularies. Banquet uses Bandsplit RNN rather than a UNet and the authors state that UNets should not be suitable for conditioned MSS. We counter this argument and propose QSCNet, a novel conditioned UNet for MSS that integrates network conditioning elements in the Sparse Compressed Network for MSS. We find QSCNet to outperform Banquet by over 1dB SNR on a couple of MSS tasks, while using less than half the number of parameters.", "date": "2025-12-17", "categories": []}
{"id": "2512.10375", "title": "Neural personal sound zones with flexible bright zone control", "abstract": "Personal sound zone (PSZ) reproduction system, which attempts to create distinct virtual acoustic scenes for different listeners at their respective positions within the same spatial area using one loudspeaker array, is a fundamental technology in the application of virtual reality. For practical applications, the reconstruction targets must be measured on the same fixed receiver array used to record the local room impulse responses (RIRs) from the loudspeaker array to the control points in each PSZ, which makes the system inconvenient and costly for real-world use. In this paper, a 3D convolutional neural network (CNN) designed for PSZ reproduction with flexible control microphone grid and alternative reproduction target is presented, utilizing the virtual target scene as inputs and the PSZ pre-filters as output. Experimental results of the proposed method are compared with the traditional method, demonstrating that the proposed method is able to handle varied reproduction targets on flexible control point grid using only one training session. Furthermore, the proposed method also demonstrates the capability to learn global spatial information from sparse sampling points distributed in PSZs.", "date": "2025-12-11", "categories": []}
{"id": "2512.06689", "title": "Lightweight Wasserstein Audio-Visual Model for Unified Speech Enhancement and Separation", "abstract": "Speech Enhancement (SE) and Speech Separation (SS) have traditionally been treated as distinct tasks in speech processing. However, real-world audio often involves both background noise and overlapping speakers, motivating the need for a unified solution. While recent approaches have attempted to integrate SE and SS within multi-stage architectures, these approaches typically involve complex, parameter-heavy models and rely on supervised training, limiting scalability and generalization. In this work, we propose UniVoiceLite, a lightweight and unsupervised audio-visual framework that unifies SE and SS within a single model. UniVoiceLite leverages lip motion and facial identity cues to guide speech extraction and employs Wasserstein distance regularization to stabilize the latent space without requiring paired noisy-clean data. Experimental results demonstrate that UniVoiceLite achieves strong performance in both noisy and multi-speaker scenarios, combining efficiency with robust generalization. The source code is available at https://github.com/jisoo-o/UniVoiceLite.", "date": "2025-12-07", "categories": []}
{"id": "2512.04793", "title": "YingMusic-SVC: Real-World Robust Zero-Shot Singing Voice Conversion with Flow-GRPO and Singing-Specific Inductive Biases", "abstract": "Singing voice conversion (SVC) aims to render the target singer's timbre while preserving melody and lyrics. However, existing zero-shot SVC systems remain fragile in real songs due to harmony interference, F0 errors, and the lack of inductive biases for singing. We propose YingMusic-SVC, a robust zero-shot framework that unifies continuous pre-training, robust supervised fine-tuning, and Flow-GRPO reinforcement learning. Our model introduces a singing-trained RVC timbre shifter for timbre-content disentanglement, an F0-aware timbre adaptor for dynamic vocal expression, and an energy-balanced rectified flow matching loss to enhance high-frequency fidelity. Experiments on a graded multi-track benchmark show that YingMusic-SVC achieves consistent improvements over strong open-source baselines in timbre similarity, intelligibility, and perceptual naturalness, especially under accompanied and harmony-contaminated conditions, demonstrating its effectiveness for real-world SVC deployment.", "date": "2025-12-04", "categories": []}
{"id": "2505.21025", "title": "Text-Queried Audio Source Separation via Hierarchical Modeling", "abstract": "Target audio source separation with natural language queries presents a promising paradigm for extracting arbitrary audio events through arbitrary text descriptions. Existing methods mainly face two challenges, the difficulty in jointly modeling acoustic-textual alignment and semantic-aware separation within a blindly-learned single-stage architecture, and the reliance on large-scale accurately-labeled training data to compensate for inefficient cross-modal learning and separation. To address these challenges, we propose a hierarchical decomposition framework, HSM-TSS, that decouples the task into global-local semantic-guided feature separation and structure-preserving acoustic reconstruction. Our approach introduces a dual-stage mechanism for semantic separation, operating on distinct global and local semantic feature spaces. We first perform global-semantic separation through a global semantic feature space aligned with text queries. A Q-Audio architecture is employed to align audio and text modalities, serving as pretrained global-semantic encoders. Conditioned on the predicted global feature, we then perform the second-stage local-semantic separation on AudioMAE features that preserve time-frequency structures, followed by acoustic reconstruction. We also propose an instruction processing pipeline to parse arbitrary text queries into structured operations, extraction or removal, coupled with audio descriptions, enabling flexible sound manipulation. Our method achieves state-of-the-art separation performance with data-efficient training while maintaining superior semantic consistency with queries in complex auditory scenes.", "date": "2025-05-27", "categories": []}
{"id": "2511.21342", "title": "Generating Separated Singing Vocals Using a Diffusion Model Conditioned on Music Mixtures", "abstract": "Separating the individual elements in a musical mixture is an essential process for music analysis and practice. While this is generally addressed using neural networks optimized to mask or transform the time-frequency representation of a mixture to extract the target sources, the flexibility and generalization capabilities of generative diffusion models are giving rise to a novel class of solutions for this complicated task. In this work, we explore singing voice separation from real music recordings using a diffusion model which is trained to generate the solo vocals conditioned on the corresponding mixture. Our approach improves upon prior generative systems and achieves competitive objective scores against non-generative baselines when trained with supplementary data. The iterative nature of diffusion sampling enables the user to control the quality-efficiency trade-off, and also refine the output when needed. We present an ablation study of the sampling algorithm, highlighting the effects of the user-configurable parameters.", "date": "2025-11-26", "categories": []}
{"id": "2511.20380", "title": "Differentiable Attenuation Filters for Feedback Delay Networks", "abstract": "We introduce a novel method for designing attenuation filters in digital audio reverberation systems based on Feedback Delay Networks (FDNs). Our approach uses Second Order Sections (SOS) of Infinite Impulse Response (IIR) filters arranged as parametric equalizers (PEQ), enabling fine control over frequency-dependent reverberation decay. Unlike traditional graphic equalizer designs, which require numerous filters per delay line, we propose a scalable solution where the number of filters can be adjusted. The frequency, gain, and quality factor (Q) parameters are shared parameters across delay lines and only the gain is adjusted based on delay length. This design not only reduces the number of optimization parameters, but also remains fully differentiable and compatible with gradient-based learning frameworks. Leveraging principles of analog filter design, our method allows for efficient and accurate filter fitting using supervised learning. Our method delivers a flexible and differentiable design, achieving state-of-the-art performance while significantly reducing computational cost.", "date": "2025-11-25", "categories": []}
{"id": "2511.20470", "title": "Efficient and Fast Generative-Based Singing Voice Separation using a Latent Diffusion Model", "abstract": "Extracting individual elements from music mixtures is a valuable tool for music production and practice. While neural networks optimized to mask or transform mixture spectrograms into the individual source(s) have been the leading approach, the source overlap and correlation in music signals poses an inherent challenge. Also, accessing all sources in the mixture is crucial to train these systems, while complicated. Attempts to address these challenges in a generative fashion exist, however, the separation performance and inference efficiency remain limited. In this work, we study the potential of diffusion models to advance toward bridging this gap, focusing on generative singing voice separation relying only on corresponding pairs of isolated vocals and mixtures for training. To align with creative workflows, we leverage latent diffusion: the system generates samples encoded in a compact latent space, and subsequently decodes these into audio. This enables efficient optimization and faster inference. Our system is trained using only open data. We outperform existing generative separation systems, and level the compared non-generative systems on a list of signal quality measures and on interference removal. We provide a noise robustness study on the latent encoder, providing insights on its potential for the task. We release a modular toolkit for further research on the topic.", "date": "2025-11-25", "categories": []}
{"id": "2511.19396", "title": "Real-Time Object Tracking with On-Device Deep Learning for Adaptive Beamforming in Dynamic Acoustic Environments", "abstract": "Advances in object tracking and acoustic beamforming are driving new capabilities in surveillance, human-computer interaction, and robotics. This work presents an embedded system that integrates deep learning-based tracking with beamforming to achieve precise sound source localization and directional audio capture in dynamic environments. The approach combines single-camera depth estimation and stereo vision to enable accurate 3D localization of moving objects. A planar concentric circular microphone array constructed with MEMS microphones provides a compact, energy-efficient platform supporting 2D beam steering across azimuth and elevation. Real-time tracking outputs continuously adapt the array's focus, synchronizing the acoustic response with the target's position. By uniting learned spatial awareness with dynamic steering, the system maintains robust performance in the presence of multiple or moving sources. Experimental evaluation demonstrates significant gains in signal-to-interference ratio, making the design well-suited for teleconferencing, smart home devices, and assistive technologies.", "date": "2025-11-24", "categories": []}
{"id": "2511.17136", "title": "Device-Guided Music Transfer", "abstract": "Device-guided music transfer adapts playback across unseen devices for users who lack them. Existing methods mainly focus on modifying the timbre, rhythm, harmony, or instrumentation to mimic genres or artists, overlooking the diverse hardware properties of the playback device (i.e., speaker). Therefore, we propose DeMT, which processes a speaker's frequency response curve as a line graph using a vision-language model to extract device embeddings. These embeddings then condition a hybrid transformer via feature-wise linear modulation. Fine-tuned on a self-collected dataset, DeMT enables effective speaker-style transfer and robust few-shot adaptation for unseen devices, supporting applications like device-style augmentation and quality enhancement.", "date": "2025-11-21", "categories": []}
{"id": "2511.17346", "title": "Is Phase Really Needed for Weakly-Supervised Dereverberation ?", "abstract": "In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating to what extent information can be retrieved from the sole knowledge of reverberant (wet) speech becomes critical. This work investigates the role of the reverberant (wet) phase in the time-frequency domain. Based on Statistical Wave Field Theory, we show that late reverberation perturbs phase components with white, uniformly distributed noise, except at low frequencies. Consequently, the wet phase carries limited useful information and is not essential for weakly supervised dereverberation. To validate this finding, we train dereverberation models under a recent weak supervision framework and demonstrate that performance can be significantly improved by excluding the reverberant phase from the loss function.", "date": "2025-11-20", "categories": []}
{"id": "2511.11473", "title": "Proactive Hearing Assistants that Isolate Egocentric Conversations", "abstract": "We introduce proactive hearing assistants that automatically identify and separate the wearer's conversation partners, without requiring explicit prompts. Our system operates on egocentric binaural audio and uses the wearer's self-speech as an anchor, leveraging turn-taking behavior and dialogue dynamics to infer conversational partners and suppress others. To enable real-time, on-device operation, we propose a dual-model architecture: a lightweight streaming model runs every 12.5 ms for low-latency extraction of the conversation partners, while a slower model runs less frequently to capture longer-range conversational dynamics. Results on real-world 2- and 3-speaker conversation test sets, collected with binaural egocentric hardware from 11 participants totaling 6.8 hours, show generalization in identifying and isolating conversational partners in multi-conversation settings. Our work marks a step toward hearing assistants that adapt proactively to conversational dynamics and engagement. More information can be found on our website: https://proactivehearing.cs.washington.edu/", "date": "2025-11-14", "categories": []}
{"id": "2511.07677", "title": "Speech Separation for Hearing-Impaired Children in the Classroom", "abstract": "Classroom environments are particularly challenging for children with hearing impairments, where background noise, multiple talkers, and reverberation degrade speech perception. These difficulties are greater for children than adults, yet most deep learning speech separation models for assistive devices are developed using adult voices in simplified, low-reverberation conditions. This overlooks both the higher spectral similarity of children's voices, which weakens separation cues, and the acoustic complexity of real classrooms. We address this gap using MIMO-TasNet, a compact, low-latency, multi-channel architecture suited for real-time deployment in bilateral hearing aids or cochlear implants. We simulated naturalistic classroom scenes with moving child-child and child-adult talker pairs under varying noise and distance conditions. Training strategies tested how well the model adapts to children's speech through spatial cues. Models trained on adult speech, classroom data, and finetuned variants were compared to assess data-efficient adaptation. Results show that adult-trained models perform well in clean scenes, but classroom-specific training greatly improves separation quality. Finetuning with only half the classroom data achieved comparable gains, confirming efficient transfer learning. Training with diffuse babble noise further enhanced robustness, and the model preserved spatial awareness while generalizing to unseen distances. These findings demonstrate that spatially aware architectures combined with targeted adaptation can improve speech accessibility for children in noisy classrooms, supporting future on-device assistive technologies.", "date": "2025-11-10", "categories": []}
{"id": "2511.08012", "title": "DOA Estimation with Lightweight Network on LLM-Aided Simulated Acoustic Scenes", "abstract": "Direction-of-Arrival (DOA) estimation is critical in spatial audio and acoustic signal processing, with wide-ranging applications in real-world. Most existing DOA models are trained on synthetic data by convolving clean speech with room impulse responses (RIRs), which limits their generalizability due to constrained acoustic diversity. In this paper, we revisit DOA estimation using a recently introduced dataset constructed with the assistance of large language models (LLMs), which provides more realistic and diverse spatial audio scenes. We benchmark several representative neural-based DOA methods on this dataset and propose LightDOA, a lightweight DOA estimation model based on depthwise separable convolutions, specifically designed for mutil-channel input in varying environments. Experimental results show that LightDOA achieves satisfactory accuracy and robustness across various acoustic scenes while maintaining low computational complexity. This study not only highlights the potential of spatial audio synthesized with the assistance of LLMs in advancing robust and efficient DOA estimation research, but also highlights LightDOA as efficient solution for resource-constrained applications.", "date": "2025-11-11", "categories": []}
{"id": "2511.06288", "title": "ELEGANCE: Efficient LLM Guidance for Audio-Visual Target Speech Extraction", "abstract": "Audio-visual target speaker extraction (AV-TSE) models primarily rely on visual cues from the target speaker. However, humans also leverage linguistic knowledge, such as syntactic constraints, next word prediction, and prior knowledge of conversation, to extract target speech. Inspired by this observation, we propose ELEGANCE, a novel framework that incorporates linguistic knowledge from large language models (LLMs) into AV-TSE models through three distinct guidance strategies: output linguistic constraints, intermediate linguistic prediction, and input linguistic prior. Comprehensive experiments with RoBERTa, Qwen3-0.6B, and Qwen3-4B on two AV-TSE backbones demonstrate the effectiveness of our approach. Significant improvements are observed in challenging scenarios, including visual cue impaired, unseen languages, target speaker switches, increased interfering speakers, and out-of-domain test set. Demo page: https://alexwxwu.github.io/ELEGANCE/.", "date": "2025-11-09", "categories": []}
{"id": "2511.05399", "title": "Robust Neural Audio Fingerprinting using Music Foundation Models", "abstract": "The proliferation of distorted, compressed, and manipulated music on modern media platforms like TikTok motivates the development of more robust audio fingerprinting techniques to identify the sources of musical recordings. In this paper, we develop and evaluate new neural audio fingerprinting techniques with the aim of improving their robustness. We make two contributions to neural fingerprinting methodology: (1) we use a pretrained music foundation model as the backbone of the neural architecture and (2) we expand the use of data augmentation to train fingerprinting models under a wide variety of audio manipulations, including time streching, pitch modulation, compression, and filtering. We systematically evaluate our methods in comparison to two state-of-the-art neural fingerprinting models: NAFP and GraFPrint. Results show that fingerprints extracted with music foundation models (e.g., MuQ, MERT) consistently outperform models trained from scratch or pretrained on non-musical audio. Segment-level evaluation further reveals their capability to accurately localize fingerprint matches, an important practical feature for catalog management.", "date": "2025-11-07", "categories": []}
{"id": "2509.14912", "title": "Back to Ear: Perceptually Driven High Fidelity Music Reconstruction", "abstract": "Variational Autoencoders (VAEs) are essential for large-scale audio tasks like diffusion-based generation. However, existing open-source models often neglect auditory perceptual aspects during training, leading to weaknesses in phase accuracy and stereophonic spatial representation. To address these challenges, we propose {\\epsilon}ar-VAE, an open-source music signal reconstruction model that rethinks and optimizes the VAE training paradigm. Our contributions are threefold: (i) A K-weighting perceptual filter applied prior to loss calculation to align the objective with auditory perception. (ii) Two novel phase losses: a Correlation Loss for stereo coherence, and a Phase Loss using its derivatives--Instantaneous Frequency and Group Delay--for precision. (iii) A new spectral supervision paradigm where magnitude is supervised by all four Mid/Side/Left/Right components, while phase is supervised only by the LR components. Experiments show {\\epsilon}ar-VAE at 44.1kHz substantially outperforms leading open-source models across diverse metrics, showing particular strength in reconstructing high-frequency harmonics and the spatial characteristics.", "date": "2025-09-18", "categories": []}
{"id": "2501.15417", "title": "AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement", "abstract": "We introduce AnyEnhance, a unified generative model for voice enhancement that processes both speech and singing voices. Based on a masked generative model, AnyEnhance is capable of handling both speech and singing voices, supporting a wide range of enhancement tasks including denoising, dereverberation, declipping, super-resolution, and target speaker extraction, all simultaneously and without fine-tuning. AnyEnhance introduces a prompt-guidance mechanism for in-context learning, which allows the model to natively accept a reference speaker's timbre. In this way, it could boost enhancement performance when a reference audio is available and enable the target speaker extraction task without altering the underlying architecture. Moreover, we also introduce a self-critic mechanism into the generative process for masked generative models, yielding higher-quality outputs through iterative self-assessment and refinement. Extensive experiments on various enhancement tasks demonstrate AnyEnhance outperforms existing methods in terms of both objective metrics and subjective listening tests. Demo audios are publicly available at https://amphionspace.github.io/anyenhance. An open-source implementation is provided at https://github.com/viewfinder-annn/anyenhance-v1-ccf-aatc.", "date": "2025-01-26", "categories": []}
{"id": "2503.02585", "title": "As Good as It KAN Get: High-Fidelity Audio Representation", "abstract": "Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for audio representation. KAN demonstrates superior perceptual performance over previous INRs, achieving the lowest Log-SpectralDistance of 1.29 and the highest Perceptual Evaluation of Speech Quality of 3.57 for 1.5 s audio. To extend KAN's utility, we propose FewSound, a hypernetwork-based architecture that enhances INR parameter updates. FewSound outperforms the state-of-the-art HyperSound, with a 33.3% improvement in MSE and 60.87% in SI-SNR. These results show KAN as a robust and adaptable audio representation with the potential for scalability and integration into various hypernetwork frameworks. The source code can be accessed at https://github.com/gmum/fewsound.git.", "date": "2025-03-04", "categories": []}
{"id": "2509.13927", "title": "DSpAST: Disentangled Representations for Spatial Audio Reasoning with Large Language Models", "abstract": "Reasoning about spatial audio with large language models requires a spatial audio encoder as an acoustic front-end to obtain audio embeddings for further processing. Such an encoder needs to capture all information required to detect the type of sound events, as well as the direction and distance of their corresponding sources. Accomplishing this with a single audio encoder is demanding as the information required for each of these tasks is mostly independent of each other. As a result, the performance obtained with a single encoder is often worse than when using task-specific audio encoders. In this work, we present DSpAST, a novel audio encoder based on SpatialAST that learns disentangled representations of spatial audio while having only 0.2% additional parameters. Experiments on SpatialSoundQA with the spatial audio reasoning system BAT demonstrate that DSpAST significantly outperforms SpatialAST.", "date": "2025-09-17", "categories": []}
{"id": "2511.01773", "title": "ADNAC: Audio Denoiser using Neural Audio Codec", "abstract": "Audio denoising is critical in signal processing, enhancing intelligibility and fidelity for applications like restoring musical recordings. This paper presents a proof-of-concept for adapting a state-of-the-art neural audio codec, the Descript Audio Codec (DAC), for music denoising. This work overcomes the limitations of traditional architectures like U-Nets by training the model on a large-scale, custom-synthesized dataset built from diverse sources. Training is guided by a multi objective loss function that combines time-domain, spectral, and signal-level fidelity metrics. Ultimately, this paper aims to present a PoC for high-fidelity, generative audio restoration.", "date": "2025-11-03", "categories": []}
{"id": "2510.26838", "title": "Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition", "abstract": "Automated monitoring of marine mammals in the St. Lawrence Estuary faces extreme challenges: calls span low-frequency moans to ultrasonic clicks, often overlap, and are embedded in variable anthropogenic and environmental noise. We introduce a multi-step, attention-guided framework that first segments spectrograms to generate soft masks of biologically relevant energy and then fuses these masks with the raw inputs for multi-band, denoised classification. Image and mask embeddings are integrated via mid-level fusion, enabling the model to focus on salient spectrogram regions while preserving global context. Using real-world recordings from the Saguenay St. Lawrence Marine Park Research Station in Canada, we demonstrate that segmentation-driven attention and mid-level fusion improve signal discrimination, reduce false positive detections, and produce reliable representations for operational marine mammal monitoring across diverse environmental conditions and signal-to-noise ratios. Beyond in-distribution evaluation, we further assess the generalization of Mask-Guided Classification (MGC) under distributional shifts by testing on spectrograms generated with alternative acoustic transformations. While high-capacity baseline models lose accuracy in this Out-of-distribution (OOD) setting, MGC maintains stable performance, with even simple fusion mechanisms (gated, concat) achieving comparable results across distributions. This robustness highlights the capacity of MGC to learn transferable representations rather than overfitting to a specific transformation, thereby reinforcing its suitability for large-scale, real-world biodiversity monitoring. We show that in all experimental settings, the MGC framework consistently outperforms baseline architectures, yielding substantial gains in accuracy on both in-distribution and OOD data.", "date": "2025-10-29", "categories": []}
{"id": "2510.24519", "title": "Audio Signal Processing Using Time Domain Mel-Frequency Wavelet Coefficient", "abstract": "Extracting features from the speech is the most critical process in speech signal processing. Mel Frequency Cepstral Coefficients (MFCC) are the most widely used features in the majority of the speaker and speech recognition applications, as the filtering in this feature is similar to the filtering taking place in the human ear. But the main drawback of this feature is that it provides only the frequency information of the signal but does not provide the information about at what time which frequency is present. The wavelet transform, with its flexible time-frequency window, provides time and frequency information of the signal and is an appropriate tool for the analysis of non-stationary signals like speech. On the other hand, because of its uniform frequency scaling, a typical wavelet transform may be less effective in analysing speech signals, have poorer frequency resolution in low frequencies, and be less in line with human auditory perception. Hence, it is necessary to develop a feature that incorporates the merits of both MFCC and wavelet transform. A great deal of studies are trying to combine both these features. The present Wavelet Transform based Mel-scaled feature extraction methods require more computation when a wavelet transform is applied on top of Mel-scale filtering, since it adds extra processing steps. Here we are proposing a method to extract Mel scale features in time domain combining the concept of wavelet transform, thus reducing the computational burden of time-frequency conversion and the complexity of wavelet extraction. Combining our proposed Time domain Mel frequency Wavelet Coefficient(TMFWC) technique with the reservoir computing methodology has significantly improved the efficiency of audio signal processing.", "date": "2025-10-28", "categories": []}
{"id": "2510.22439", "title": "PromptReverb: Multimodal Room Impulse Response Generation Through Latent Rectified Flow Matching", "abstract": "Room impulse response (RIR) generation remains a critical challenge for creating immersive virtual acoustic environments. Current methods suffer from two fundamental limitations: the scarcity of full-band RIR datasets and the inability of existing models to generate acoustically accurate responses from diverse input modalities. We present PromptReverb, a two-stage generative framework that addresses these challenges. Our approach combines a variational autoencoder that upsamples band-limited RIRs to full-band quality (48 kHz), and a conditional diffusion transformer model based on rectified flow matching that generates RIRs from descriptions in natural language. Empirical evaluation demonstrates that PromptReverb produces RIRs with superior perceptual quality and acoustic accuracy compared to existing methods, achieving 8.8% mean RT60 error compared to -37% for widely used baselines and yielding more realistic room-acoustic parameters. Our method enables practical applications in virtual reality, architectural acoustics, and audio production where flexible, high-quality RIR synthesis is essential.", "date": "2025-10-25", "categories": []}
{"id": "2507.16343", "title": "Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries", "abstract": "Most existing sound event detection~(SED) algorithms operate under a closed-set assumption, restricting their detection capabilities to predefined classes. While recent efforts have explored language-driven zero-shot SED by exploiting audio-language models, their performance is still far from satisfactory due to the lack of fine-grained alignment and cross-modal feature fusion. In this work, we propose the Detect Any Sound Model (DASM), a query-based framework for open-vocabulary SED guided by multi-modal queries. DASM formulates SED as a frame-level retrieval task, where audio features are matched against query vectors derived from text or audio prompts. To support this formulation, DASM introduces a dual-stream decoder that explicitly decouples event recognition and temporal localization: a cross-modality event decoder performs query-feature fusion and determines the presence of sound events at the clip-level, while a context network models temporal dependencies for frame-level localization. Additionally, an inference-time attention masking strategy is proposed to leverage semantic relations between base and novel classes, substantially enhancing generalization to novel classes. Experiments on the AudioSet Strong dataset demonstrate that DASM effectively balances localization accuracy with generalization to novel classes, outperforming CLAP-based methods in open-vocabulary setting (+ 7.8 PSDS) and the baseline in the closed-set setting (+ 6.9 PSDS). Furthermore, in cross-dataset zero-shot evaluation on DESED, DASM achieves a PSDS1 score of 42.2, even exceeding the supervised CRNN baseline. The project page is available at https://cai525.github.io/Transformer4SED/demo_page/DASM/.", "date": "2025-07-22", "categories": []}
{"id": "2508.01488", "title": "PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objective", "abstract": "In this paper, we introduce PESTO, a self-supervised learning approach for single-pitch estimation using a Siamese architecture. Our model processes individual frames of a Variable-$Q$ Transform (VQT) and predicts pitch distributions. The neural network is designed to be equivariant to translations, notably thanks to a Toeplitz fully-connected layer. In addition, we construct pitch-shifted pairs by translating and cropping the VQT frames and train our model with a novel class-based transposition-equivariant objective, eliminating the need for annotated data. Thanks to this architecture and training objective, our model achieves remarkable performances while being very lightweight ($130$k parameters). Evaluations on music and speech datasets (MIR-1K, MDB-stem-synth, and PTDB) demonstrate that PESTO not only outperforms self-supervised baselines but also competes with supervised methods, exhibiting superior cross-dataset generalization. Finally, we enhance PESTO's practical utility by developing a streamable VQT implementation using cached convolutions. Combined with our model's low latency (less than 10 ms) and minimal parameter count, this makes PESTO particularly suitable for real-time applications.", "date": "2025-08-02", "categories": []}
{"id": "2508.01493", "title": "Translation-Equivariant Self-Supervised Learning for Pitch Estimation with Optimal Transport", "abstract": "In this paper, we propose an Optimal Transport objective for learning one-dimensional translation-equivariant systems and demonstrate its applicability to single pitch estimation. Our method provides a theoretically grounded, more numerically stable, and simpler alternative for training state-of-the-art self-supervised pitch estimators.", "date": "2025-08-02", "categories": []}
{"id": "2510.11507", "title": "Automatic Music Sample Identification with Multi-Track Contrastive Learning", "abstract": "Sampling, the technique of reusing pieces of existing audio tracks to create new music content, is a very common practice in modern music production. In this paper, we tackle the challenging task of automatic sample identification, that is, detecting such sampled content and retrieving the material from which it originates. To do so, we adopt a self-supervised learning approach that leverages a multi-track dataset to create positive pairs of artificial mixes, and design a novel contrastive learning objective. We show that such method significantly outperforms previous state-of-the-art baselines, that is robust to various genres, and that scales well when increasing the number of noise songs in the reference database. In addition, we extensively analyze the contribution of the different components of our training pipeline and highlight, in particular, the need for high-quality separated stems for this task.", "date": "2025-10-13", "categories": []}
{"id": "2510.20602", "title": "Resounding Acoustic Fields with Reciprocity", "abstract": "Achieving immersive auditory experiences in virtual environments requires flexible sound modeling that supports dynamic source positions. In this paper, we introduce a task called resounding, which aims to estimate room impulse responses at arbitrary emitter location from a sparse set of measured emitter positions, analogous to the relighting problem in vision. We leverage the reciprocity property and introduce Versa, a physics-inspired approach to facilitating acoustic field learning. Our method creates physically valid samples with dense virtual emitter positions by exchanging emitter and listener poses. We also identify challenges in deploying reciprocity due to emitter/listener gain patterns and propose a self-supervised learning approach to address them. Results show that Versa substantially improve the performance of acoustic field learning on both simulated and real-world datasets across different metrics. Perceptual user studies show that Versa can greatly improve the immersive spatial sound experience. Code, dataset and demo videos are available on the project website: https://waves.seas.upenn.edu/projects/versa.", "date": "2025-10-23", "categories": []}
{"id": "2505.23305", "title": "MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction", "abstract": "We present MGE-LDM, a unified latent diffusion framework for simultaneous music generation, source imputation, and query-driven source separation. Unlike prior approaches constrained to fixed instrument classes, MGE-LDM learns a joint distribution over full mixtures, submixtures, and individual stems within a single compact latent diffusion model. At inference, MGE-LDM enables (1) complete mixture generation, (2) partial generation (i.e., source imputation), and (3) text-conditioned extraction of arbitrary sources. By formulating both separation and imputation as conditional inpainting tasks in the latent space, our approach supports flexible, class-agnostic manipulation of arbitrary instrument sources. Notably, MGE-LDM can be trained jointly across heterogeneous multi-track datasets (e.g., Slakh2100, MUSDB18, MoisesDB) without relying on predefined instrument categories. Audio samples are available at our project page: https://yoongi43.github.io/MGELDM_Samples/.", "date": "2025-05-29", "categories": []}
{"id": "2505.11315", "title": "Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior", "abstract": "Style Transfer with Inference-Time Optimisation (ST-ITO) is a recent approach for transferring the applied effects of a reference audio to an audio track. It optimises the effect parameters to minimise the distance between the style embeddings of the processed audio and the reference. However, this method treats all possible configurations equally and relies solely on the embedding space, which can result in unrealistic configurations or biased outcomes. We address this pitfall by introducing a Gaussian prior derived from the DiffVox vocal preset dataset over the parameter space. The resulting optimisation is equivalent to maximum-a-posteriori estimation. Evaluations on vocal effects transfer on the MedleyDB dataset show significant improvements across metrics compared to baselines, including a blind audio effects estimator, nearest-neighbour approaches, and uncalibrated ST-ITO. The proposed calibration reduces the parameter mean squared error by up to 33% and more closely matches the reference style. Subjective evaluations with 16 participants confirm the superiority of our method in limited data regimes. This work demonstrates how incorporating prior knowledge at inference time enhances audio effects transfer, paving the way for more effective and realistic audio processing systems.", "date": "2025-05-16", "categories": []}
{"id": "2510.15383", "title": "DroneAudioset: An Audio Dataset for Drone-based Search and Rescue", "abstract": "Unmanned Aerial Vehicles (UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence. Existing datasets are either limited in diversity or synthetic, lacking real acoustic interactions, and there are no standardized setups for drone audition. To this end, we present DroneAudioset (The dataset is publicly available at https://huggingface.co/datasets/ahlab-drone-project/DroneAudioSet/ under the MIT license), a comprehensive drone audition dataset featuring 23.5 hours of annotated recordings, covering a wide range of signal-to-noise ratios (SNRs) from -57.2 dB to -2.5 dB, across various drone types, throttles, microphone configurations as well as environments. The dataset enables development and systematic evaluation of noise suppression and classification methods for human-presence detection under challenging conditions, while also informing practical design considerations for drone audition systems, such as microphone placement trade-offs, and development of drone noise-aware audio processing. This dataset is an important step towards enabling design and deployment of drone-audition systems.", "date": "2025-10-17", "categories": []}
{"id": "2509.15666", "title": "TISDiSS: A Training-Time and Inference-Time Scalable Framework for Discriminative Source Separation", "abstract": "Source separation is a fundamental task in speech, music, and audio processing, and it also provides cleaner and larger data for training generative models. However, improving separation performance in practice often depends on increasingly large networks, inflating training and deployment costs. Motivated by recent advances in inference-time scaling for generative modeling, we propose Training-Time and Inference-Time Scalable Discriminative Source Separation (TISDiSS), a unified framework that integrates early-split multi-loss supervision, shared-parameter design, and dynamic inference repetitions. TISDiSS enables flexible speed-performance trade-offs by adjusting inference depth without retraining additional models. We further provide systematic analyses of architectural and training choices and show that training with more inference repetitions improves shallow-inference performance, benefiting low-latency applications. Experiments on standard speech separation benchmarks demonstrate state-of-the-art performance with a reduced parameter count, establishing TISDiSS as a scalable and practical framework for adaptive source separation. Code is available at https://github.com/WingSingFung/TISDiSS.", "date": "2025-09-19", "categories": []}
{"id": "2510.09926", "title": "Phase-Aware Deep Learning with Complex-Valued CNNs for Audio Signal Applications", "abstract": "This study explores the design and application of Complex-Valued Convolutional Neural Networks (CVCNNs) in audio signal processing, with a focus on preserving and utilizing phase information often neglected in real-valued networks. We begin by presenting the foundational theoretical concepts of CVCNNs, including complex convolutions, pooling layers, Wirtinger-based differentiation, and various complex-valued activation functions. These are complemented by critical adaptations of training techniques, including complex batch normalization and weight initialization schemes, to ensure stability in training dynamics. Empirical evaluations are conducted across three stages. First, CVCNNs are benchmarked on standard image datasets, where they demonstrate competitive performance with real-valued CNNs, even under synthetic complex perturbations. Although our focus is audio signal processing, we first evaluate CVCNNs on image datasets to establish baseline performance and validate training stability before applying them to audio tasks. In the second experiment, we focus on audio classification using Mel-Frequency Cepstral Coefficients (MFCCs). CVCNNs trained on real-valued MFCCs slightly outperform real CNNs, while preserving phase in input workflows highlights challenges in exploiting phase without architectural modifications. Finally, a third experiment introduces GNNs to model phase information via edge weighting, where the inclusion of phase yields measurable gains in both binary and multi-class genre classification. These results underscore the expressive capacity of complex-valued architectures and confirm phase as a meaningful and exploitable feature in audio processing applications. While current methods show promise, especially with activations like cardioid, future advances in phase-aware design will be essential to leverage the potential of complex representations in neural networks.", "date": "2025-10-10", "categories": []}
{"id": "2510.10687", "title": "LSZone: A Lightweight Spatial Information Modeling Architecture for Real-time In-car Multi-zone Speech Separation", "abstract": "In-car multi-zone speech separation, which captures voices from different speech zones, plays a crucial role in human-vehicle interaction. Although previous SpatialNet has achieved notable results, its high computational cost still hinders real-time applications in vehicles. To this end, this paper proposes LSZone, a lightweight spatial information modeling architecture for real-time in-car multi-zone speech separation. We design a spatial information extraction-compression (SpaIEC) module that combines Mel spectrogram and Interaural Phase Difference (IPD) to reduce computational burden while maintaining performance. Additionally, to efficiently model spatial information, we introduce an extremely lightweight Conv-GRU crossband-narrowband processing (CNP) module. Experimental results demonstrate that LSZone, with a complexity of 0.56G MACs and a real-time factor (RTF) of 0.37, delivers impressive performance in complex noise and multi-speaker scenarios.", "date": "2025-10-12", "categories": []}
{"id": "2510.07503", "title": "Time-Frequency Filtering Meets Graph Clustering", "abstract": "We show that the problem of identifying different signal components from a time-frequency representation can be equivalently phrased as a graph clustering problem: given a graph $G=(V,E)$ one aims to identify `clusters', subgraphs that are strongly connected and have relatively few connections between them. The graph clustering problem is well studied, we show how these ideas can suggest (many) new ways to identify signal components. Numerical experiments illustrate the ideas.", "date": "2025-10-08", "categories": []}
{"id": "2510.00264", "title": "Baseline Systems For The 2025 Low-Resource Audio Codec Challenge", "abstract": "The Low-Resource Audio Codec (LRAC) Challenge aims to advance neural audio coding for deployment in resource-constrained environments. The first edition focuses on low-resource neural speech codecs that must operate reliably under everyday noise and reverberation, while satisfying strict constraints on computational complexity, latency, and bitrate. Track 1 targets transparency codecs, which aim to preserve the perceptual transparency of input speech under mild noise and reverberation. Track 2 addresses enhancement codecs, which combine coding and compression with denoising and dereverberation. This paper presents the official baseline systems for both tracks in the 2025 LRAC Challenge. The baselines are convolutional neural codec models with Residual Vector Quantization, trained end-to-end using a combination of adversarial and reconstruction objectives. We detail the data filtering and augmentation strategies, model architectures, optimization procedures, and checkpoint selection criteria.", "date": "2025-09-30", "categories": []}
{"id": "2510.05542", "title": "Sci-Phi: A Large Language Model Spatial Audio Descriptor", "abstract": "Acoustic scene perception involves describing the type of sounds, their timing, their direction and distance, as well as their loudness and reverberation. While audio language models excel in sound recognition, single-channel input fundamentally limits spatial understanding. This work presents Sci-Phi, a spatial audio large language model with dual spatial and spectral encoders that estimates a complete parameter set for all sound sources and the surrounding environment. Learning from over 4,000 hours of synthetic first-order Ambisonics recordings including metadata, Sci-Phi enumerates and describes up to four directional sound sources in one pass, alongside non-directional background sounds and room characteristics. We evaluate the model with a permutation-invariant protocol and 15 metrics covering content, location, timing, loudness, and reverberation, and analyze its robustness across source counts, signal-to-noise ratios, reverberation levels, and challenging mixtures of acoustically, spatially, or temporally similar sources. Notably, Sci-Phi generalizes to real room impulse responses with only minor performance degradation. Overall, this work establishes the first audio LLM capable of full spatial-scene description, with strong potential for real-world deployment. Demo: https://sci-phi-audio.github.io/demo", "date": "2025-10-07", "categories": []}
{"id": "2510.06204", "title": "Modulation Discovery with Differentiable Digital Signal Processing", "abstract": "Modulations are a critical part of sound design and music production, enabling the creation of complex and evolving audio. Modern synthesizers provide envelopes, low frequency oscillators (LFOs), and more parameter automation tools that allow users to modulate the output with ease. However, determining the modulation signals used to create a sound is difficult, and existing sound-matching / parameter estimation systems are often uninterpretable black boxes or predict high-dimensional framewise parameter values without considering the shape, structure, and routing of the underlying modulation curves. We propose a neural sound-matching approach that leverages modulation extraction, constrained control signal parameterizations, and differentiable digital signal processing (DDSP) to discover the modulations present in a sound. We demonstrate the effectiveness of our approach on highly modulated synthetic and real audio samples, its applicability to different DDSP synth architectures, and investigate the trade-off it incurs between interpretability and sound-matching accuracy. We make our code and audio samples available and provide the trained DDSP synths in a VST plugin.", "date": "2025-10-07", "categories": []}
{"id": "2510.01968", "title": "Multi-bit Audio Watermarking", "abstract": "We present Timbru, a post-hoc audio watermarking model that achieves state-of-the-art robustness and imperceptibility trade-offs without training an embedder-detector model. Given any 44.1 kHz stereo music snippet, our method performs per-audio gradient optimization to add imperceptible perturbations in the latent space of a pretrained audio VAE, guided by a combined message and perceptual loss. The watermark can then be extracted using a pretrained CLAP model. We evaluate 16-bit watermarking on MUSDB18-HQ against AudioSeal, WavMark, and SilentCipher across common filtering, noise, compression, resampling, cropping, and regeneration attacks. Our approach attains the best average bit error rates, while preserving perceptual quality, demonstrating an efficient, dataset-free path to imperceptible audio watermarking.", "date": "2025-10-02", "categories": []}
{"id": "2509.26564", "title": "Parametric Neural Amp Modeling with Active Learning", "abstract": "We introduce Panama, an active learning framework to train parametric guitar amp models end-to-end using a combination of an LSTM model and a WaveNet-like architecture. With \\model, one can create a virtual amp by recording samples that are determined through an ensemble-based active learning strategy to minimize the amount of datapoints needed (i.e., amp knob settings). Our strategy uses gradient-based optimization to maximize the disagreement among ensemble models, in order to identify the most informative datapoints. MUSHRA listening tests reveal that, with 75 datapoints, our models are able to match the perceptual quality of NAM, the leading open-source non-parametric amp modeler.", "date": "2025-09-30", "categories": []}
{"id": "2509.26580", "title": "Source Separation for A Cappella Music", "abstract": "In this work, we study the task of multi-singer separation in a cappella music, where the number of active singers varies across mixtures. To address this, we use a power set-based data augmentation strategy that expands limited multi-singer datasets into exponentially more training samples. To separate singers, we introduce SepACap, an adaptation of SepReformer, a state-of-the-art speaker separation model architecture. We adapt the model with periodic activations and a composite loss function that remains effective when stems are silent, enabling robust detection and separation. Experiments on the JaCappella dataset demonstrate that our approach achieves state-of-the-art performance in both full-ensemble and subset singer separation scenarios, outperforming spectrogram-based baselines while generalizing to realistic mixtures with varying numbers of singers.", "date": "2025-09-30", "categories": []}
{"id": "2509.24404", "title": "From Sound to Setting: AI-Based Equalizer Parameter Prediction for Piano Tone Replication", "abstract": "This project presents an AI-based system for tone replication in music production, focusing on predicting EQ parameter settings directly from audio features. Unlike traditional audio-to-audio methods, our approach outputs interpretable parameter values (e.g., EQ band gains) that musicians can further adjust in their workflow. Using a dataset of piano recordings with systematically varied EQ settings, we evaluate both regression and neural network models. The neural network achieves a mean squared error of 0.0216 on multi-band tasks. The system enables practical, flexible, and automated tone matching for music producers and lays the foundation for extensions to more complex audio effects.", "date": "2025-09-29", "categories": []}
{"id": "2509.22063", "title": "High-Quality Sound Separation Across Diverse Categories via Visually-Guided Generative Modeling", "abstract": "We propose DAVIS, a Diffusion-based Audio-VIsual Separation framework that solves the audio-visual sound source separation task through generative learning. Existing methods typically frame sound separation as a mask-based regression problem, achieving significant progress. However, they face limitations in capturing the complex data distribution required for high-quality separation of sounds from diverse categories. In contrast, DAVIS circumvents these issues by leveraging potent generative modeling paradigms, specifically Denoising Diffusion Probabilistic Models (DDPM) and the more recent Flow Matching (FM), integrated within a specialized Separation U-Net architecture. Our framework operates by synthesizing the desired separated sound spectrograms directly from a noise distribution, conditioned concurrently on the mixed audio input and associated visual information. The inherent nature of its generative objective makes DAVIS particularly adept at producing high-quality sound separations for diverse sound categories. We present comparative evaluations of DAVIS, encompassing both its DDPM and Flow Matching variants, against leading methods on the standard AVE and MUSIC datasets. The results affirm that both variants surpass existing approaches in separation quality, highlighting the efficacy of our generative framework for tackling the audio-visual source separation task.", "date": "2025-09-26", "categories": []}
{"id": "2509.18412", "title": "Identifying birdsong syllables without labelled data", "abstract": "Identifying sequences of syllables within birdsongs is key to tackling a wide array of challenges, including bird individual identification and better understanding of animal communication and sensory-motor learning. Recently, machine learning approaches have demonstrated great potential to alleviate the need for experts to label long audio recordings by hand. However, they still typically rely on the availability of labelled data for model training, restricting applicability to a few species and datasets. In this work, we build the first fully unsupervised algorithm to decompose birdsong recordings into sequences of syllables. We first detect syllable events, then cluster them to extract templates -- syllable representations -- before performing matching pursuit to decompose the recording as a sequence of syllables. We evaluate our automatic annotations against human labels on a dataset of Bengalese finch songs and find that our unsupervised method achieves high performance. We also demonstrate that our approach can distinguish individual birds within a species through their unique vocal signatures, for both Bengalese finches and another species, the great tit.", "date": "2025-09-22", "categories": []}
{"id": "2504.04751", "title": "Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches", "abstract": "Accurately estimating nonlinear audio effects without access to paired input-output signals remains a challenging problem. This work studies unsupervised probabilistic approaches for solving this task. We introduce a method, novel for this application, based on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using black- and gray-box models. This study compares this method with a previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the effect operator and varying lengths of available effected recordings. Through experiments on guitar distortion effects, we show that the diffusion-based approach provides more stable results and is less sensitive to data availability, while the adversarial approach is superior at estimating more pronounced distortion effects. Our findings contribute to the robust unsupervised blind estimation of audio effects, demonstrating the potential of diffusion models for system identification in music technology.", "date": "2025-04-07", "categories": []}
{"id": "2509.16342", "title": "Similarity-Guided Diffusion for Long-Gap Music Inpainting", "abstract": "Music inpainting aims to reconstruct missing segments of a corrupted recording. While diffusion-based generative models improve reconstruction for medium-length gaps, they often struggle to preserve musical plausibility over multi-second gaps. We introduce Similarity-Guided Diffusion Posterior Sampling (SimDPS), a hybrid method that combines diffusion-based inference with similarity search. Candidate segments are first retrieved from a corpus based on contextual similarity, then incorporated into a modified likelihood that guides the diffusion process toward contextually consistent reconstructions. Subjective evaluation on piano music inpainting with 2-s gaps shows that the proposed SimDPS method enhances perceptual plausibility compared to unguided diffusion and frequently outperforms similarity search alone when moderately similar candidates are available. These results demonstrate the potential of a hybrid similarity approach for diffusion-based audio enhancement with long gaps.", "date": "2025-09-19", "categories": []}
{"id": "2509.16926", "title": "Cross-Attention with Confidence Weighting for Multi-Channel Audio Alignment", "abstract": "Multi-channel audio alignment is a key requirement in bioacoustic monitoring, spatial audio systems, and acoustic localization. However, existing methods often struggle to address nonlinear clock drift and lack mechanisms for quantifying uncertainty. Traditional methods like Cross-correlation and Dynamic Time Warping assume simple drift patterns and provide no reliability measures. Meanwhile, recent deep learning models typically treat alignment as a binary classification task, overlooking inter-channel dependencies and uncertainty estimation. We introduce a method that combines cross-attention mechanisms with confidence-weighted scoring to improve multi-channel audio synchronization. We extend BEATs encoders with cross-attention layers to model temporal relationships between channels. We also develop a confidence-weighted scoring function that uses the full prediction distribution instead of binary thresholding. Our method achieved first place in the BioDCASE 2025 Task 1 challenge with 0.30 MSE average across test datasets, compared to 0.58 for the deep learning baseline. On individual datasets, we achieved 0.14 MSE on ARU data (77% reduction) and 0.45 MSE on zebra finch data (18% reduction). The framework supports probabilistic temporal alignment, moving beyond point estimates. While validated in a bioacoustic context, the approach is applicable to a broader range of multi-channel audio tasks where alignment confidence is critical. Code available on: https://github.com/Ragib-Amin-Nihal/BEATsCA", "date": "2025-09-21", "categories": []}
{"id": "2509.17219", "title": "Virtual Consistency for Audio Editing", "abstract": "Free-form, text-based audio editing remains a persistent challenge, despite progress in inversion-based neural methods. Current approaches rely on slow inversion procedures, limiting their practicality. We present a virtual-consistency based audio editing system that bypasses inversion by adapting the sampling process of diffusion models. Our pipeline is model-agnostic, requiring no fine-tuning or architectural changes, and achieves substantial speed-ups over recent neural editing baselines. Crucially, it achieves this efficiency without compromising quality, as demonstrated by quantitative benchmarks and a user study involving 16 participants.", "date": "2025-09-21", "categories": []}
{"id": "2509.17883", "title": "Brainprint-Modulated Target Speaker Extraction", "abstract": "Achieving robust and personalized performance in neuro-steered Target Speaker Extraction (TSE) remains a significant challenge for next-generation hearing aids. This is primarily due to two factors: the inherent non-stationarity of EEG signals across sessions, and the high inter-subject variability that limits the efficacy of generalized models. To address these issues, we propose Brainprint-Modulated Target Speaker Extraction (BM-TSE), a novel framework for personalized and high-fidelity extraction. BM-TSE first employs a spatio-temporal EEG encoder with an Adaptive Spectral Gain (ASG) module to extract stable features resilient to non-stationarity. The core of our framework is a personalized modulation mechanism, where a unified brainmap embedding is learned under the joint supervision of subject identification (SID) and auditory attention decoding (AAD) tasks. This learned brainmap, encoding both static user traits and dynamic attentional states, actively refines the audio separation process, dynamically tailoring the output to each user. Evaluations on the public KUL and Cocktail Party datasets demonstrate that BM-TSE achieves state-of-the-art performance, significantly outperforming existing methods. Our code is publicly accessible at: https://github.com/rosshan-orz/BM-TSE.", "date": "2025-09-22", "categories": []}
{"id": "2507.07318", "title": "Generating Moving 3D Soundscapes with Latent Diffusion Models", "abstract": "Spatial audio has become central to immersive applications such as VR/AR, cinema, and music. Existing generative audio models are largely limited to mono or stereo formats and cannot capture the full 3D localization cues available in first-order Ambisonics (FOA). Recent FOA models extend text-to-audio generation but remain restricted to static sources. In this work, we introduce SonicMotion, the first end-to-end latent diffusion framework capable of generating FOA audio with explicit control over moving sound sources. SonicMotion is implemented in two variations: 1) a descriptive model conditioned on natural language prompts, and 2) a parametric model conditioned on both text and spatial trajectory parameters for higher precision. To support training and evaluation, we construct a new dataset of over one million simulated FOA caption pairs that include both static and dynamic sources with annotated azimuth, elevation, and motion attributes. Experiments show that SonicMotion achieves state-of-the-art semantic alignment and perceptual quality comparable to leading text-to-audio systems, while uniquely attaining low spatial localization error.", "date": "2025-07-09", "categories": []}
{"id": "2509.14379", "title": "Diffusion-Based Unsupervised Audio-Visual Speech Separation in Noisy Environments with Noise Prior", "abstract": "In this paper, we address the problem of single-microphone speech separation in the presence of ambient noise. We propose a generative unsupervised technique that directly models both clean speech and structured noise components, training exclusively on these individual signals rather than noisy mixtures. Our approach leverages an audio-visual score model that incorporates visual cues to serve as a strong generative speech prior. By explicitly modelling the noise distribution alongside the speech distribution, we enable effective decomposition through the inverse problem paradigm. We perform speech separation by sampling from the posterior distributions via a reverse diffusion process, which directly estimates and removes the modelled noise component to recover clean constituent signals. Experimental results demonstrate promising performance, highlighting the effectiveness of our direct noise modelling approach in challenging acoustic environments.", "date": "2025-09-17", "categories": []}
{"id": "2509.14666", "title": "Spatial Audio Motion Understanding and Reasoning", "abstract": "Spatial audio reasoning enables machines to interpret auditory scenes by understanding events and their spatial attributes. In this work, we focus on spatial audio understanding with an emphasis on reasoning about moving sources. First, we introduce a spatial audio encoder that processes spatial audio to detect multiple overlapping events and estimate their spatial attributes, Direction of Arrival (DoA) and source distance, at the frame level. To generalize to unseen events, we incorporate an audio grounding model that aligns audio features with semantic audio class text embeddings via a cross-attention mechanism. Second, to answer complex queries about dynamic audio scenes involving moving sources, we condition a large language model (LLM) on structured spatial attributes extracted by our model. Finally, we introduce a spatial audio motion understanding and reasoning benchmark dataset and demonstrate our framework's performance against the baseline model.", "date": "2025-09-18", "categories": []}
{"id": "2509.15140", "title": "FCPE: A Fast Context-based Pitch Estimation Model", "abstract": "Pitch estimation (PE) in monophonic audio is crucial for MIDI transcription and singing voice conversion (SVC), but existing methods suffer significant performance degradation under noise. In this paper, we propose FCPE, a fast context-based pitch estimation model that employs a Lynx-Net architecture with depth-wise separable convolutions to effectively capture mel spectrogram features while maintaining low computational cost and robust noise tolerance. Experiments show that our method achieves 96.79\\% Raw Pitch Accuracy (RPA) on the MIR-1K dataset, on par with the state-of-the-art methods. The Real-Time Factor (RTF) is 0.0062 on a single RTX 4090 GPU, which significantly outperforms existing algorithms in efficiency. Code is available at https://github.com/CNChTu/FCPE.", "date": "2025-09-18", "categories": []}
{"id": "2507.06070", "title": "Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol", "abstract": "Recent advances in song identification leverage deep neural networks to learn compact audio fingerprints directly from raw waveforms. While these methods perform well under controlled conditions, their accuracy drops significantly in real-world scenarios where the audio is captured via mobile devices in noisy environments. In this paper, we introduce a novel evaluation protocol designed to better reflect such real-world conditions. We generate three recordings of the same audio, each with increasing levels of noise, captured using a mobile device's microphone. Our results reveal a substantial performance drop for two state-of-the-art CNN-based models under this protocol, compared to previously reported benchmarks. Additionally, we highlight the critical role of the augmentation pipeline during training with contrastive loss. By introduction low pass and high pass filters in the augmentation pipeline we significantly increase the performance of both systems in our proposed evaluation. Furthermore, we develop a transformer-based model with a tailored projection module and demonstrate that transferring knowledge from a semantically relevant domain yields a more robust solution. The transformer architecture outperforms CNN-based models across all noise levels, and query durations. In low noise conditions it achieves 47.99% for 1-sec queries, and 97% for 10-sec queries in finding the correct song, surpassing by 14%, and by 18.5% the second-best performing model, respectively, Under heavy noise levels, we achieve a detection rate 56.5% for 15-second query duration. All experiments are conducted on public large-scale dataset of over 100K songs, with queries matched against a database of 56 million vectors.", "date": "2025-07-08", "categories": []}
{"id": "2509.02622", "title": "IS${}^3$ : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering", "abstract": "We are interested in audio systems capable of performing a differentiated processing of stationary backgrounds and isolated acoustic events within an acoustic scene, whether for applying specific processing methods to each part or for focusing solely on one while ignoring the other. Such systems have applications in real-world scenarios, including robust adaptive audio rendering systems (e.g., EQ or compression), plosive attenuation in voice mixing, noise suppression or reduction, robust acoustic event classification or even bioacoustics. To this end, we introduce IS${}^3$, a neural network designed for Impulsive--Stationary Sound Separation, that isolates impulsive acoustic events from the stationary background using a deep filtering approach, that can act as a pre-processing stage for the above-mentioned tasks. To ensure optimal training, we propose a sophisticated data generation pipeline that curates and adapts existing datasets for this task. We demonstrate that a learning-based approach, build on a relatively lightweight neural architecture and trained with well-designed and varied data, is successful in this previously unaddressed task, outperforming the Harmonic--Percussive Sound Separation masking method, adapted from music signal processing research, and wavelet filtering on objective separation metrics.", "date": "2025-09-01", "categories": []}
{"id": "2509.09836", "title": "CoDiCodec: Unifying Continuous and Discrete Compressed Representations of Audio", "abstract": "Efficiently representing audio signals in a compressed latent space is critical for latent generative modelling. However, existing autoencoders often force a choice between continuous embeddings and discrete tokens. Furthermore, achieving high compression ratios while maintaining audio fidelity remains a challenge. We introduce CoDiCodec, a novel audio autoencoder that overcomes these limitations by both efficiently encoding global features via summary embeddings, and by producing both compressed continuous embeddings at ~ 11 Hz and discrete tokens at a rate of 2.38 kbps from the same trained model, offering unprecedented flexibility for different downstream generative tasks. This is achieved through Finite Scalar Quantization (FSQ) and a novel FSQ-dropout technique, and does not require additional loss terms beyond the single consistency loss used for end-to-end training. CoDiCodec supports both autoregressive decoding and a novel parallel decoding strategy, with the latter achieving superior audio quality and faster decoding. CoDiCodec outperforms existing continuous and discrete autoencoders at similar bitrates in terms of reconstruction audio quality. Our work enables a unified approach to audio compression, bridging the gap between continuous and discrete generative modelling paradigms.", "date": "2025-09-11", "categories": []}
{"id": "2508.11609", "title": "Pretrained Conformers for Audio Fingerprinting and Retrieval", "abstract": "Conformers have shown great results in speech processing due to their ability to capture both local and global interactions. In this work, we utilize a self-supervised contrastive learning framework to train conformer-based encoders that are capable of generating unique embeddings for small segments of audio, generalizing well to previously unseen data. We achieve state-of-the-art results for audio retrieval tasks while using only 3 seconds of audio to generate embeddings. Our models are almost completely immune to temporal misalignments and achieve state-of-the-art results in cases of other audio distortions such as noise, reverb or extreme temporal stretching. Code and models are made publicly available and the results are easy to reproduce as we train and test using popular and freely available datasets of different sizes.", "date": "2025-08-15", "categories": []}
{"id": "2411.04337", "title": "Neural-Enhanced Dynamic Range Compression Inversion: A Hybrid Approach for Restoring Audio Dynamics", "abstract": "Dynamic Range Compression (DRC) is a widely used audio effect that adjusts signal dynamics for applications in music production, broadcasting, and speech processing. Inverting DRC is of broad importance for restoring the original dynamics, enabling remixing, and enhancing the overall audio quality. Existing DRC inversion methods either overlook key parameters or rely on precise parameter values, which can be challenging to estimate accurately. To address this limitation, we introduce a hybrid approach that combines model-based DRC inversion with neural networks to achieve robust DRC parameter estimation and audio restoration simultaneously. Our method uses tailored neural network architectures (classification and regression), which are then integrated into a model-based inversion framework to reconstruct the original signal. Experimental evaluations on various music and speech datasets confirm the effectiveness and robustness of our approach, outperforming several state-of-the-art techniques.", "date": "2024-11-07", "categories": []}
{"id": "2502.07205", "title": "VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification", "abstract": "Reverberant speech, denoting the speech signal degraded by reverberation, contains crucial knowledge of both anechoic source speech and room impulse response (RIR). This work proposes a variational Bayesian inference (VBI) framework with neural speech prior (VINP) for joint speech dereverberation and blind RIR identification. In VINP, a probabilistic signal model is constructed in the time-frequency (T-F) domain based on convolution transfer function (CTF) approximation. For the first time, we propose using an arbitrary discriminative dereverberation deep neural network (DNN) to estimate the prior distribution of anechoic speech within a probabilistic model. By integrating both reverberant speech and the anechoic speech prior, VINP yields the maximum a posteriori (MAP) and maximum likelihood (ML) estimations of the anechoic speech spectrum and CTF filter, respectively. After simple transformations, the waveforms of anechoic speech and RIR are estimated. VINP is effective for automatic speech recognition (ASR) systems, which sets it apart from most deep learning (DL)-based single-channel dereverberation approaches. Experiments on single-channel speech dereverberation demonstrate that VINP attains state-of-the-art (SOTA) performance in mean opinion score (MOS) and word error rate (WER). For blind RIR identification, experiments demonstrate that VINP achieves SOTA performance in estimating reverberation time at 60 dB (RT60) and advanced performance in direct-to-reverberation ratio (DRR) estimation. Codes and audio samples are available online.", "date": "2025-02-11", "categories": []}
{"id": "2506.00681", "title": "Learning to Upsample and Upmix Audio in the Latent Domain", "abstract": "Neural audio autoencoders create compact latent representations that preserve perceptually important information, serving as the foundation for both modern audio compression systems and generation approaches like next-token prediction and latent diffusion. Despite their prevalence, most audio processing operations, such as spatial and spectral up-sampling, still inefficiently operate on raw waveforms or spectral representations rather than directly on these compressed representations. We propose a framework that performs audio processing operations entirely within an autoencoder's latent space, eliminating the need to decode to raw audio formats. Our approach dramatically simplifies training by operating solely in the latent domain, with a latent L1 reconstruction term, augmented by a single latent adversarial discriminator. This contrasts sharply with raw-audio methods that typically require complex combinations of multi-scale losses and discriminators. Through experiments in bandwidth extension and mono-to-stereo up-mixing, we demonstrate computational efficiency gains of up to 100x while maintaining quality comparable to post-processing on raw audio. This work establishes a more efficient paradigm for audio processing pipelines that already incorporate autoencoders, enabling significantly faster and more resource-efficient workflows across various audio tasks.", "date": "2025-05-31", "categories": []}
{"id": "2509.07586", "title": "Exploring System Adaptations For Minimum Latency Real-Time Piano Transcription", "abstract": "Advances in neural network design and the availability of large-scale labeled datasets have driven major improvements in piano transcription. Existing approaches target either offline applications, with no restrictions on computational demands, or online transcription, with delays of 128-320 ms. However, most real-time musical applications require latencies below 30 ms. In this work, we investigate whether and how the current state-of-the-art online transcription model can be adapted for real-time piano transcription. Specifically, we eliminate all non-causal processing, and reduce computational load through shared computations across core model components and variations in model size. Additionally, we explore different pre- and postprocessing strategies, and related label encoding schemes, and discuss their suitability for real-time transcription. Evaluating the adaptions on the MAESTRO dataset, we find a drop in transcription accuracy due to strictly causal processing as well as a tradeoff between the preprocessing latency and prediction accuracy. We release our system as a baseline to support researchers in designing models towards minimum latency real-time transcription.", "date": "2025-09-09", "categories": []}
{"id": "2509.07635", "title": "Neural Proxies for Sound Synthesizers: Learning Perceptually Informed Preset Representations", "abstract": "Deep learning appears as an appealing solution for Automatic Synthesizer Programming (ASP), which aims to assist musicians and sound designers in programming sound synthesizers. However, integrating software synthesizers into training pipelines is challenging due to their potential non-differentiability. This work tackles this challenge by introducing a method to approximate arbitrary synthesizers. Specifically, we train a neural network to map synthesizer presets onto an audio embedding space derived from a pretrained model. This facilitates the definition of a neural proxy that produces compact yet effective representations, thereby enabling the integration of audio embedding loss into neural-based ASP systems for black-box synthesizers. We evaluate the representations derived by various pretrained audio models in the context of neural-based nASP and assess the effectiveness of several neural network architectures, including feedforward, recurrent, and transformer-based models, in defining neural proxies. We evaluate the proposed method using both synthetic and hand-crafted presets from three popular software synthesizers and assess its performance in a synthesizer sound matching downstream task. While the benefits of the learned representation are nuanced by resource requirements, encouraging results were obtained for all synthesizers, paving the way for future research into the application of synthesizer proxies for neural-based ASP systems.", "date": "2025-09-09", "categories": []}
{"id": "2509.05256", "title": "Recomposer: Event-roll-guided generative audio editing", "abstract": "Editing complex real-world sound scenes is difficult because individual sound sources overlap in time. Generative models can fill-in missing or corrupted details based on their strong prior understanding of the data domain. We present a system for editing individual sound events within complex scenes able to delete, insert, and enhance individual sound events based on textual edit descriptions (e.g., ``enhance Door'') and a graphical representation of the event timing derived from an ``event roll'' transcription. We present an encoder-decoder transformer working on SoundStream representations, trained on synthetic (input, desired output) audio example pairs formed by adding isolated sound events to dense, real-world backgrounds. Evaluation reveals the importance of each part of the edit descriptions -- action, class, timing. Our work demonstrates ``recomposition'' is an important and practical application.", "date": "2025-09-05", "categories": []}
{"id": "2502.10822", "title": "NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids", "abstract": "The prevalence of hearing aids is increasing. However, optimizing the amplification processes of hearing aids remains challenging due to the complexity of integrating multiple modular components in traditional methods. To address this challenge, we present NeuroAMP, a novel deep neural network designed for end-to-end, personalized amplification in hearing aids. NeuroAMP leverages both spectral features and the listener's audiogram as inputs, and we investigate four architectures: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Convolutional Recurrent Neural Network (CRNN), and Transformer. We also introduce Denoising NeuroAMP, an extension that integrates noise reduction along with amplification capabilities for improved performance in real-world scenarios. To enhance generalization, a comprehensive data augmentation strategy was employed during training on diverse speech (TIMIT and TMHINT) and music (Cadenza Challenge MUSIC) datasets. Evaluation using the Hearing Aid Speech Perception Index (HASPI), Hearing Aid Speech Quality Index (HASQI), and Hearing Aid Audio Quality Index (HAAQI) demonstrates that the Transformer architecture within NeuroAMP achieves the best performance, with SRCC scores of 0.9927 (HASQI) and 0.9905 (HASPI) on TIMIT, and 0.9738 (HAAQI) on the Cadenza Challenge MUSIC dataset. Notably, our data augmentation strategy maintains high performance on unseen datasets (e.g., VCTK, MUSDB18-HQ). Furthermore, Denoising NeuroAMP outperforms both the conventional NAL-R+WDRC approach and a two-stage baseline on the VoiceBank+DEMAND dataset, achieving a 10% improvement in both HASPI (0.90) and HASQI (0.59) scores. These results highlight the potential of NeuroAMP and Denoising NeuroAMP to deliver notable improvements in personalized hearing aid amplification.", "date": "2025-02-15", "categories": []}
{"id": "2509.01399", "title": "CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays", "abstract": "Separating overlapping speech from multiple speakers is crucial for effective human-vehicle interaction. This paper proposes CabinSep, a lightweight neural mask-based minimum variance distortionless response (MVDR) speech separation approach, to reduce speech recognition errors in back-end automatic speech recognition (ASR) models. Our contributions are threefold: First, we utilize channel information to extract spatial features, which improves the estimation of speech and noise masks. Second, we employ MVDR during inference, reducing speech distortion to make it more ASR-friendly. Third, we introduce a data augmentation method combining simulated and real-recorded impulse responses (IRs), improving speaker localization at zone boundaries and further reducing speech recognition errors. With a computational complexity of only 0.4 GMACs, CabinSep achieves a 17.5% relative reduction in speech recognition error rate in a real-recorded dataset compared to the state-of-the-art DualSep model. Demos are available at: https://cabinsep.github.io/cabinsep/.", "date": "2025-09-01", "categories": []}
{"id": "2509.02471", "title": "ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection", "abstract": "The core challenge in industrial equipment anoma lous sound detection (ASD) lies in modeling the time-frequency coupling characteristics of acoustic features. Existing modeling methods are limited by local receptive fields, making it difficult to capture long-range temporal patterns and cross-band dynamic coupling effects in machine acoustic features. In this paper, we propose a novel framework, ESTM, which is based on a dual-path Mamba architecture with time-frequency decoupled modeling and utilizes Selective State-Space Models (SSM) for long-range sequence modeling. ESTM extracts rich feature representations from different time segments and frequency bands by fusing enhanced Mel spectrograms and raw audio features, while further improving sensitivity to anomalous patterns through the TriStat-Gating (TSG) module. Our experiments demonstrate that ESTM improves anomalous detection performance on the DCASE 2020 Task 2 dataset, further validating the effectiveness of the proposed method.", "date": "2025-09-02", "categories": []}
{"id": "2410.01108", "title": "Augmentation through Laundering Attacks for Audio Spoof Detection", "abstract": "Recent text-to-speech (TTS) developments have made voice cloning (VC) more realistic, affordable, and easily accessible. This has given rise to many potential abuses of this technology, including Joe Biden's New Hampshire deepfake robocall. Several methodologies have been proposed to detect such clones. However, these methodologies have been trained and evaluated on relatively clean databases. Recently, ASVspoof 5 Challenge introduced a new crowd-sourced database of diverse acoustic conditions including various spoofing attacks and codec conditions. This paper is our submission to the ASVspoof 5 Challenge and aims to investigate the performance of Audio Spoof Detection, trained using data augmentation through laundering attacks, on the ASVSpoof 5 database. The results demonstrate that our system performs worst on A18, A19, A20, A26, and A30 spoofing attacks and in the codec and compression conditions of C08, C09, and C10.", "date": "2024-10-01", "categories": []}
{"id": "2508.20584", "title": "Flowing Straighter with Conditional Flow Matching for Accurate Speech Enhancement", "abstract": "Current flow-based generative speech enhancement methods learn curved probability paths which model a mapping between clean and noisy speech. Despite impressive performance, the implications of curved probability paths are unknown. Methods such as Schrodinger bridges focus on curved paths, where time-dependent gradients and variance do not promote straight paths. Findings in machine learning research suggest that straight paths, such as conditional flow matching, are easier to train and offer better generalisation. In this paper we quantify the effect of path straightness on speech enhancement quality. We report experiments with the Schrodinger bridge, where we show that certain configurations lead to straighter paths. Conversely, we propose independent conditional flow-matching for speech enhancement, which models straight paths between noisy and clean speech. We demonstrate empirically that a time-independent variance has a greater effect on sample quality than the gradient. Although conditional flow matching improves several speech quality metrics, it requires multiple inference steps. We rectify this with a one-step solution by inferring the trained flow-based model as if it was directly predictive. Our work suggests that straighter time-independent probability paths improve generative speech enhancement over curved time-dependent paths.", "date": "2025-08-28", "categories": []}
{"id": "2508.20618", "title": "Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation", "abstract": "We develop a stochastic algorithm for independent component analysis that incorporates multi-trial supervision, which is available in many scientific contexts. The method blends a proximal gradient-type algorithm in the space of invertible matrices with joint learning of a prediction model through backpropagation. We illustrate the proposed algorithm on synthetic and real data experiments. In particular, owing to the additional supervision, we observe an increased success rate of the non-convex optimization and the improved interpretability of the independent components.", "date": "2025-08-28", "categories": []}
{"id": "2508.18440", "title": "SwiftF0: Fast and Accurate Monophonic Pitch Detection", "abstract": "Accurate and real-time monophonic pitch estimation in noisy conditions, particularly on resource-constrained devices, remains an open challenge in audio processing. We present \\emph{SwiftF0}, a novel, lightweight neural model that sets a new state-of-the-art for monophonic pitch estimation. Through training on diverse speech, music, and synthetic datasets with extensive data augmentation, SwiftF0 achieves robust generalization across acoustic domains while maintaining computational efficiency. SwiftF0 achieves a 91.80\\% harmonic mean (HM) at 10 dB SNR, outperforming baselines like CREPE by over 12 percentage points and degrading by only 2.3 points from clean audio. SwiftF0 requires only 95,842 parameters and runs approximately 42x faster than CREPE on CPU, making it ideal for efficient, real-time deployment. To address the critical lack of perfectly accurate ground truth pitch in speech corpora (which typically rely on algorithmic estimators or laryngograph signals), we introduce \\emph{SpeechSynth}. This synthetic speech dataset, generated by a phoneme-level TTS model, provides exact, on-demand ground-truth pitch curves, enabling more robust model training and evaluation. Furthermore, we propose a unified metric, combining six complementary performance measures for comprehensive and reliable pitch evaluation, and release an open-source pitch benchmark suite. A live demo of SwiftF0 is available at https://swift-f0.github.io/, the source code at https://github.com/lars76/swift-f0, and the benchmark framework at https://github.com/lars76/pitch-benchmark.", "date": "2025-08-25", "categories": []}
{"id": "2508.12009", "title": "Optimizing Neural Architectures for Hindi Speech Separation and Enhancement in Noisy Environments", "abstract": "This paper addresses the challenges of Hindi speech separation and enhancement using advanced neural network architectures, with a focus on edge devices. We propose a refined approach leveraging the DEMUCS model to overcome limitations of traditional methods, achieving substantial improvements in speech clarity and intelligibility. The model is fine-tuned with U-Net and LSTM layers, trained on a dataset of 400,000 Hindi speech clips augmented with ESC-50 and MS-SNSD for diverse acoustic environments. Evaluation using PESQ and STOI metrics shows superior performance, particularly under extreme noise conditions. To ensure deployment on resource-constrained devices like TWS earbuds, we explore quantization techniques to reduce computational requirements. This research highlights the effectiveness of customized AI algorithms for speech processing in Indian contexts and suggests future directions for optimizing edge-based architectures.", "date": "2025-08-16", "categories": []}
{"id": "2505.11378", "title": "Machine Learning Approaches to Vocal Register Classification in Contemporary Male Pop Music", "abstract": "For singers of all experience levels, one of the most daunting challenges in learning technical repertoire is navigating placement and vocal register in and around the passagio (passage between chest voice and head voice registers). Particularly in pop music, where a single artist may use a variety of timbre's and textures to achieve a desired quality, it can be difficult to identify what vocal register within the vocal range a singer is using. This paper presents two methods for classifying vocal registers in an audio signal of male pop music through the analysis of textural features of mel-spectrogram images. Additionally, we will discuss the practical integration of these models for vocal analysis tools, and introduce a concurrently developed software called AVRA which stands for Automatic Vocal Register Analysis. Our proposed methods achieved consistent classification of vocal register through both Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models, which supports the promise of more robust classification possibilities across more voice types and genres of singing.", "date": "2025-05-16", "categories": []}
{"id": "2508.14919", "title": "Denoising by neural network for muzzle blast detection", "abstract": "Acoem develops gunshot detection systems, consisting of a microphone array and software that detects and locates shooters on the battlefield.  The performance of such systems is obviously affected by the acoustic environment in which they are operating: in particular, when mounted on a moving military vehicle, the presence of noise reduces the detection performance of the software. To limit the influence of the acoustic environment, a neural network has been developed. Instead of using a heavy convolutional neural network, a lightweight neural network architecture was chosen to limit the computational resources required to embed the algorithm on as many hardware platforms as possible.  Thanks to the combination of a two hidden layer perceptron and appropriate signal processing techniques, the detection rate of impulsive muzzle blast waveforms (the wave coming from the detonation and indicating the position of the shooter) is significantly increased. With a rms value of noise of the same order as the muzzle blast peak amplitude, the detect rate is more than doubled with this denoising processing.", "date": "2025-08-18", "categories": []}
{"id": "2507.02755", "title": "Multi-agent Auditory Scene Analysis", "abstract": "Auditory scene analysis (ASA) aims to retrieve information from the acoustic environment, by carrying out three main tasks: sound source location, separation, and classification. These tasks are traditionally executed with a linear data flow, where the sound sources are first located; then, using their location, each source is separated into its own audio stream; from each of which, information is extracted that is relevant to the application scenario (audio event detection, speaker identification, emotion classification, etc.). However, running these tasks linearly increases the overall response time, while making the last tasks (separation and classification) highly sensitive to errors of the first task (location). A considerable amount of effort and computational complexity has been employed in the state-of-the-art to develop techniques that are the least error-prone possible. However, doing so gives rise to an ASA system that is non-viable in many applications that require a small computational footprint and a low response time, such as bioacoustics, hearing-aid design, search and rescue, human-robot interaction, etc. To this effect, in this work, a multi-agent approach is proposed to carry out ASA where the tasks are run in parallel, with feedback loops between them to compensate for local errors, such as: using the quality of the separation output to correct the location error; and using the classification result to reduce the localization's sensitivity towards interferences. The result is a multi-agent auditory scene analysis (MASA) system that is robust against local errors, without a considerable increase in complexity, and with a low response time. The complete proposed MASA system is provided as a publicly available framework that uses open-source tools for sound acquisition and reproduction (JACK) and inter-agent communication (ROS2), allowing users to add their own agents.", "date": "2025-07-03", "categories": []}
{"id": "2505.23509", "title": "Spectrotemporal Modulation: Efficient and Interpretable Feature Representation for Classifying Speech, Music, and Environmental Sounds", "abstract": "Audio DNNs have demonstrated impressive performance on various machine listening tasks; however, most of their representations are computationally costly and uninterpretable, leaving room for optimization. Here, we propose a novel approach centered on spectrotemporal modulation (STM) features, a signal processing method that mimics the neurophysiological representation in the human auditory cortex. The classification performance of our STM-based model, without any pretraining, is comparable to that of pretrained audio DNNs across diverse naturalistic speech, music, and environmental sounds, which are essential categories for both human cognition and machine perception. These results show that STM is an efficient and interpretable feature representation for audio classification, advancing the development of machine listening and unlocking exciting new possibilities for basic understanding of speech and auditory sciences, as well as developing audio BCI and cognitive computing.", "date": "2025-05-29", "categories": []}
{"id": "2504.07402", "title": "LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models", "abstract": "We propose LauraTSE, an Auto-Regressive Decoder-Only Language Model for Target Speaker Extraction built upon the LauraGPT backbone. LauraTSE employs a small-scale auto-regressive decoder-only language model that generates the initial layers of the target speech's discrete codec representations from the continuous embeddings of both the mixture and reference speech. These outputs serve as coarse-grained predictions. To refine them, a one-step encoder-only language model reconstructs the full codec representation by integrating information from both the mixture and the reference speech, adding fine-grained details. Experimental results show that our approach can achieve promising performance. Additionally, we conduct ablation studies to investigate the data scalability and the contribution of the encoder-only model.", "date": "2025-04-10", "categories": []}
{"id": "2508.13018", "title": "Design and Analysis of Robust Adaptive Filtering with the Hyperbolic Tangent Exponential Kernel M-Estimator Function for Active Noise Control", "abstract": "In this work, we propose a robust adaptive filtering approach for active noise control applications in the presence of impulsive noise. In particular, we develop the filtered-x hyperbolic tangent exponential generalized Kernel M-estimate function (FXHEKM) robust adaptive algorithm. A statistical analysis of the proposed FXHEKM algorithm is carried out along with a study of its computational cost. {In order to evaluate the proposed FXHEKM algorithm, the mean-square error (MSE) and the average noise reduction (ANR) performance metrics have been adopted.} Numerical results show the efficiency of the proposed FXHEKM algorithm to cancel the presence of the additive spurious signals, such as \\textbf{$\\alpha$}-stable noises against competing algorithms.", "date": "2025-08-18", "categories": []}
{"id": "2504.04949", "title": "L3AC: Towards a Lightweight and Lossless Audio Codec", "abstract": "Neural audio codecs have recently gained traction for their ability to compress high-fidelity audio and provide discrete tokens for generative modeling. However, leading approaches often rely on resource-intensive models and complex multi-quantizer architectures, limiting their practicality in real-world applications. In this work, we introduce L3AC, a lightweight neural audio codec that addresses these challenges by leveraging a single quantizer and a highly efficient architecture. To enhance reconstruction fidelity while minimizing model complexity, L3AC explores streamlined convolutional networks and local Transformer modules, alongside TConv--a novel structure designed to capture acoustic variations across multiple temporal scales. Despite its compact design, extensive experiments across diverse datasets demonstrate that L3AC matches or exceeds the reconstruction quality of leading codecs while reducing computational overhead by an order of magnitude. The single-quantizer design further enhances its adaptability for downstream tasks. The source code is publicly available at https://github.com/zhai-lw/L3AC.", "date": "2025-04-07", "categories": []}
{"id": "2501.11311", "title": "A2SB: Audio-to-Audio Schrodinger Bridges", "abstract": "Real-world audio is often degraded by numerous factors. This work presents an audio restoration model tailored for high-res music at 44.1kHz. Our model, Audio-to-Audio Schr\\\"odinger Bridges (A2SB), is capable of both bandwidth extension (predicting high-frequency components) and inpainting (re-generating missing segments). Critically, A2SB is end-to-end requiring no vocoder to predict waveform outputs, able to restore hour-long audio inputs, and trained on permissively licensed music data. A2SB is capable of achieving state-of-the-art band-width extension and inpainting quality on several out-of-distribution music test sets.", "date": "2025-01-20", "categories": []}
{"id": "2508.07561", "title": "A Small-footprint Acoustic Echo Cancellation Solution for Mobile Full-Duplex Speech Interactions", "abstract": "In full-duplex speech interaction systems, effective Acoustic Echo Cancellation (AEC) is crucial for recovering echo-contaminated speech. This paper presents a neural network-based AEC solution to address challenges in mobile scenarios with varying hardware, nonlinear distortions and long latency. We first incorporate diverse data augmentation strategies to enhance the model's robustness across various environments. Moreover, progressive learning is employed to incrementally improve AEC effectiveness, resulting in a considerable improvement in speech quality. To further optimize AEC's downstream applications, we introduce a novel post-processing strategy employing tailored parameters designed specifically for tasks such as Voice Activity Detection (VAD) and Automatic Speech Recognition (ASR), thus enhancing their overall efficacy. Finally, our method employs a small-footprint model with streaming inference, enabling seamless deployment on mobile devices. Empirical results demonstrate effectiveness of the proposed method in Echo Return Loss Enhancement and Perceptual Evaluation of Speech Quality, alongside significant improvements in both VAD and ASR results.", "date": "2025-08-11", "categories": []}
{"id": "2504.21772", "title": "Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration Deep Learning Pipeline", "abstract": "Short video platforms like YouTube Shorts and TikTok face significant copyright compliance challenges, as infringers frequently embed arbitrary background music (BGM) to obscure original soundtracks (OST) and evade content originality detection. To tackle this issue, we propose a novel pipeline that integrates Music Source Separation (MSS) and cross-modal video-music retrieval (CMVMR). Our approach effectively separates arbitrary BGM from the original OST, enabling the restoration of authentic video audio tracks. To support this work, we introduce two domain-specific datasets: OASD-20K for audio separation and OSVAR-160 for pipeline evaluation. OASD-20K contains 20,000 audio clips featuring mixed BGM and OST pairs, while OSVAR-160 is a unique benchmark dataset comprising 1,121 video and mixed-audio pairs, specifically designed for short video restoration tasks. Experimental results demonstrate that our pipeline not only removes arbitrary BGM with high accuracy but also restores OSTs, ensuring content integrity. This approach provides an ethical and scalable solution to copyright challenges in user-generated content on short video platforms.", "date": "2025-04-30", "categories": []}
{"id": "2508.05207", "title": "SpectroStream: A Versatile Neural Codec for General Audio", "abstract": "We propose SpectroStream, a full-band multi-channel neural audio codec. Successor to the well-established SoundStream, SpectroStream extends its capability beyond 24 kHz monophonic audio and enables high-quality reconstruction of 48 kHz stereo music at bit rates of 4--16 kbps. This is accomplished with a new neural architecture that leverages audio representation in the time-frequency domain, which leads to better audio quality especially at higher sample rate. The model also uses a delayed-fusion strategy to handle multi-channel audio, which is crucial in balancing per-channel acoustic quality and cross-channel phase consistency.", "date": "2025-08-07", "categories": []}
{"id": "2405.04124", "title": "Comparative Study of State-based Neural Networks for Virtual Analog Audio Effects Modeling", "abstract": "Artificial neural networks are a promising technique for virtual analog modeling, having shown particular success in emulating distortion circuits. Despite their potential, enhancements are needed to enable effect parameters to influence the network's response and to achieve a low-latency output. While hybrid solutions, which incorporate both analytical and black-box techniques, offer certain advantages, black-box approaches, such as neural networks, can be preferable in contexts where rapid deployment, simplicity, or adaptability are required, and where understanding the internal mechanisms of the system is less critical. In this article, we explore the application of recent machine learning advancements for virtual analog modeling. We compare State-Space models and Linear Recurrent Units against the more common LSTM networks, with a variety of audio effects. We evaluate the performance and limitations of these models using multiple metrics, providing insights for future research and development. Our metrics aim to assess the models' ability to accurately replicate the signal's energy and frequency contents, with a particular focus on transients. The Feature-wise Linear Modulation method is employed to incorporate effect parameters that influence the network's response, enabling dynamic adaptability based on specified conditions. Experimental results suggest that LSTM networks offer an advantage in emulating distortions and equalizers, although performance differences are sometimes subtle yet statistically significant. On the other hand, encoder-decoder configurations of Long Short-Term Memory networks and State-Space models excel in modeling saturation and compression, effectively managing the dynamic aspects inherent in these effects. However, no models effectively emulate the low-pass filter, and Linear Recurrent Units show inconsistent performance across various audio effects.", "date": "2024-05-07", "categories": []}
{"id": "2501.02786", "title": "CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation", "abstract": "Binaural audio generation (BAG) aims to convert monaural audio to stereo audio using visual prompts, requiring a deep understanding of spatial and semantic information. However, current models risk overfitting to room environments and lose fine-grained spatial details. In this paper, we propose a new audio-visual binaural generation model incorporating an audio-visual conditional normalisation layer that dynamically aligns the mean and variance of the target difference audio features using visual context, along with a new contrastive learning method to enhance spatial sensitivity by mining negative samples from shuffled visual features. We also introduce a cost-efficient way to utilise test-time augmentation in video data to enhance performance. Our approach achieves state-of-the-art generation accuracy on the FAIR-Play and MUSIC-Stereo benchmarks.", "date": "2025-01-06", "categories": []}
{"id": "2507.01339", "title": "User-guided Generative Source Separation", "abstract": "Music source separation (MSS) aims to extract individual instrument sources from their mixture. While most existing methods focus on the widely adopted four-stem separation setup (vocals, bass, drums, and other instruments), this approach lacks the flexibility needed for real-world applications. To address this, we propose GuideSep, a diffusion-based MSS model capable of instrument-agnostic separation beyond the four-stem setup. GuideSep is conditioned on multiple inputs: a waveform mimicry condition, which can be easily provided by humming or playing the target melody, and mel-spectrogram domain masks, which offer additional guidance for separation. Unlike prior approaches that relied on fixed class labels or sound queries, our conditioning scheme, coupled with the generative approach, provides greater flexibility and applicability. Additionally, we design a mask-prediction baseline using the same model architecture to systematically compare predictive and generative approaches. Our objective and subjective evaluations demonstrate that GuideSep achieves high-quality separation while enabling more versatile instrument extraction, highlighting the potential of user participation in the diffusion-based generative process for MSS. Our code and demo page are available at https://yutongwen.github.io/GuideSep/", "date": "2025-07-02", "categories": []}
{"id": "2507.21202", "title": "Combolutional Neural Networks", "abstract": "Selecting appropriate inductive biases is an essential step in the design of machine learning models, especially when working with audio, where even short clips may contain millions of samples. To this end, we propose the combolutional layer: a learned-delay IIR comb filter and fused envelope detector, which extracts harmonic features in the time domain. We demonstrate the efficacy of the combolutional layer on three information retrieval tasks, evaluate its computational cost relative to other audio frontends, and provide efficient implementations for training. We find that the combolutional layer is an effective replacement for convolutional layers in audio tasks where precise harmonic analysis is important, e.g., piano transcription, speaker classification, and key detection. Additionally, the combolutional layer has several other key benefits over existing frontends, namely: low parameter count, efficient CPU inference, strictly real-valued computations, and improved interpretability.", "date": "2025-07-28", "categories": []}
{"id": "2508.02905", "title": "How Would It Sound? Material-Controlled Multimodal Acoustic Profile Generation for Indoor Scenes", "abstract": "How would the sound in a studio change with a carpeted floor and acoustic tiles on the walls? We introduce the task of material-controlled acoustic profile generation, where, given an indoor scene with specific audio-visual characteristics, the goal is to generate a target acoustic profile based on a user-defined material configuration at inference time. We address this task with a novel encoder-decoder approach that encodes the scene's key properties from an audio-visual observation and generates the target Room Impulse Response (RIR) conditioned on the material specifications provided by the user. Our model enables the generation of diverse RIRs based on various material configurations defined dynamically at inference time. To support this task, we create a new benchmark, the Acoustic Wonderland Dataset, designed for developing and evaluating material-aware RIR prediction methods under diverse and challenging settings. Our results demonstrate that the proposed model effectively encodes material information and generates high-fidelity RIRs, outperforming several baselines and state-of-the-art methods.", "date": "2025-08-04", "categories": []}
{"id": "2508.03047", "title": "TF-MLPNet: Tiny Real-Time Neural Speech Separation", "abstract": "Speech separation on hearable devices can enable transformative augmented and enhanced hearing capabilities. However, state-of-the-art speech separation networks cannot run in real-time on tiny, low-power neural accelerators designed for hearables, due to their limited compute capabilities. We present TF-MLPNet, the first speech separation network capable of running in real-time on such low-power accelerators while outperforming existing streaming models for blind speech separation and target speech extraction. Our network operates in the time-frequency domain, processing frequency sequences with stacks of fully connected layers that alternate along the channel and frequency dimensions, and independently processing the time sequence at each frequency bin using convolutional layers. Results show that our mixed-precision quantization-aware trained (QAT) model can process 6 ms audio chunks in real-time on the GAP9 processor, achieving a 3.5-4x runtime reduction compared to prior speech separation models.", "date": "2025-08-05", "categories": []}
{"id": "2507.07043", "title": "Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellation", "abstract": "The integration of artificial intelligence into hearing assistance marks a paradigm shift from traditional amplification-based systems to intelligent, context-aware audio processing. This systematic literature review evaluates advances in AI-driven selective noise cancellation (SNC) for hearing aids, highlighting technological evolution, implementation challenges, and future research directions. We synthesize findings across deep learning architectures, hardware deployment strategies, clinical validation studies, and user-centric design. The review traces progress from early machine learning models to state-of-the-art deep networks, including Convolutional Recurrent Networks for real-time inference and Transformer-based architectures for high-accuracy separation. Key findings include significant gains over traditional methods, with recent models achieving up to 18.3 dB SI-SDR improvement on noisy-reverberant benchmarks, alongside sub-10 ms real-time implementations and promising clinical outcomes. Yet, challenges remain in bridging lab-grade models with real-world deployment - particularly around power constraints, environmental variability, and personalization. Identified research gaps include hardware-software co-design, standardized evaluation protocols, and regulatory considerations for AI-enhanced hearing devices. Future work must prioritize lightweight models, continual learning, contextual-based classification and clinical translation to realize transformative hearing solutions for millions globally.", "date": "2025-06-25", "categories": []}
{"id": "2508.02391", "title": "Inference-time Scaling for Diffusion-based Audio Super-resolution", "abstract": "Diffusion models have demonstrated remarkable success in generative tasks, including audio super-resolution (SR). In many applications like movie post-production and album mastering, substantial computational budgets are available for achieving superior audio quality. However, while existing diffusion approaches typically increase sampling steps to improve quality, the performance remains fundamentally limited by the stochastic nature of the sampling process, leading to high-variance and quality-limited outputs. Here, rather than simply increasing the number of sampling steps, we propose a different paradigm through inference-time scaling for SR, which explores multiple solution trajectories during the sampling process. Different task-specific verifiers are developed, and two search algorithms, including the random search and zero-order search for SR, are introduced. By actively guiding the exploration of the high-dimensional solution space through verifier-algorithm combinations, we enable more robust and higher-quality outputs. Through extensive validation across diverse audio domains (speech, music, sound effects) and frequency ranges, we demonstrate consistent performance gains, achieving improvements of up to 9.70% in aesthetics, 5.88% in speaker similarity, 15.20% in word error rate, and 46.98% in spectral distance for speech SR from 4kHz to 24kHz, showcasing the effectiveness of our approach. Audio samples are available at: https://racerk.github.io/tt-scale-audiosr/.", "date": "2025-08-04", "categories": []}
{"id": "2508.02643", "title": "CAK: Emergent Audio Effects from Minimal Deep Learning", "abstract": "We demonstrate that a single 3x3 convolutional kernel can produce emergent audio effects when trained on 200 samples from a personalized corpus. We achieve this through two key techniques: (1) Conditioning Aware Kernels (CAK), where output = input + (learned_pattern x control), with a soft-gate mechanism supporting identity preservation at zero control; and (2) AuGAN (Audit GAN), which reframes adversarial training from \"is this real?\" to \"did you apply the requested value?\" Rather than learning to generate or detect forgeries, our networks cooperate to verify control application, discovering unique transformations. The learned kernel exhibits a diagonal structure creating frequency-dependent temporal shifts that are capable of producing musical effects based on input characteristics. Our results show the potential of adversarial training to discover audio transformations from minimal data, enabling new approaches to effect design.", "date": "2025-08-04", "categories": []}
{"id": "2211.15406", "title": "Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning", "abstract": "Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring. Ecoacoustics offers the advantages of non-invasive, long-duration sampling of environmental sounds and has the potential to become the reference tool for biodiversity surveying. However, the analysis and interpretation of acoustic data is a time-consuming process that often requires a great amount of human supervision. This issue might be tackled by exploiting modern techniques for automatic audio signal analysis, which have recently achieved impressive performance thanks to the advances in deep learning research. In this paper we show that convolutional neural networks can indeed significantly outperform traditional automatic methods in a challenging detection task: identification of dolphin whistles from underwater audio recordings. The proposed system can detect signals even in the presence of ambient noise, at the same time consistently reducing the likelihood of producing false positives and false negatives. Our results further support the adoption of artificial intelligence technology to improve the automatic monitoring of marine ecosystems.", "date": "2022-11-28", "categories": []}
{"id": "2506.06732", "title": "Neural Spectral Band Generation for Audio Coding", "abstract": "Spectral band replication (SBR) enables bit-efficient coding by generating high-frequency bands from the low-frequency ones. However, it only utilizes coarse spectral features upon a subband-wise signal replication, limiting adaptability to diverse acoustic signals. In this paper, we explore the efficacy of a deep neural network (DNN)-based generative approach for coding the high-frequency bands, which we call neural spectral band generation (n-SBG). Specifically, we propose a DNN-based encoder-decoder structure to extract and quantize the side information related to the high-frequency components and generate the components given both the side information and the decoded core-band signals. The whole coding pipeline is optimized with generative adversarial criteria to enable the generation of perceptually plausible sound. From experiments using AAC as the core codec, we show that the proposed method achieves a better perceptual quality than HE-AAC-v1 with much less side information.", "date": "2025-06-07", "categories": []}
{"id": "2507.19202", "title": "Latent Granular Resynthesis using Neural Audio Codecs", "abstract": "We introduce a novel technique for creative audio resynthesis that operates by reworking the concept of granular synthesis at the latent vector level. Our approach creates a \"granular codebook\" by encoding a source audio corpus into latent vector segments, then matches each latent grain of a target audio signal to its closest counterpart in the codebook. The resulting hybrid sequence is decoded to produce audio that preserves the target's temporal structure while adopting the source's timbral characteristics. This technique requires no model training, works with diverse audio materials, and naturally avoids the discontinuities typical of traditional concatenative synthesis through the codec's implicit interpolation during decoding. We include supplementary material at https://github.com/naotokui/latentgranular/ , as well as a proof-of-concept implementation to allow users to experiment with their own sounds at https://huggingface.co/spaces/naotokui/latentgranular .", "date": "2025-07-25", "categories": []}
{"id": "2505.23782", "title": "4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification", "abstract": "Unmanned aerial vehicle (UAV) usage is expected to surge in the coming decade, raising the need for heightened security measures to prevent airspace violations and security threats. This study investigates deep learning approaches to UAV classification focusing on the key issue of data scarcity. To investigate this we opted to train the models using a total of 4,500 seconds of audio samples, evenly distributed across a 9-class dataset. We leveraged parameter efficient fine-tuning (PEFT) and data augmentations to mitigate the data scarcity. This paper implements and compares the use of convolutional neural networks (CNNs) and attention-based transformers. Our results show that, CNNs outperform transformers by 1-2\\% accuracy, while still being more computationally efficient. These early findings, however, point to potential in using transformers models; suggesting that with more data and further optimizations they could outperform CNNs. Future works aims to upscale the dataset to better understand the trade-offs between these approaches.", "date": "2025-05-21", "categories": []}
{"id": "2503.17141", "title": "HiFi-Stream: Streaming Speech Enhancement with Generative Adversarial Networks", "abstract": "Speech Enhancement techniques have become core technologies in mobile devices and voice software. Still, modern deep learning solutions often require high amount of computational resources what makes their usage on low-resource devices challenging. We present HiFi-Stream, an optimized version of recently published HiFi++ model. Our experiments demonstrate that HiFi-Stream saves most of the qualities of the original model despite its size and computational complexity improved in comparison to the original HiFi++ making it one of the smallest and fastest models available. The model is evaluated in streaming setting where it demonstrates its superior performance in comparison to modern baselines.", "date": "2025-03-21", "categories": []}
{"id": "2507.17297", "title": "On Temporal Guidance and Iterative Refinement in Audio Source Separation", "abstract": "Spatial semantic segmentation of sound scenes (S5) involves the accurate identification of active sound classes and the precise separation of their sources from complex acoustic mixtures. Conventional systems rely on a two-stage pipeline - audio tagging followed by label-conditioned source separation - but are often constrained by the absence of fine-grained temporal information critical for effective separation. In this work, we address this limitation by introducing a novel approach for S5 that enhances the synergy between the event detection and source separation stages. Our key contributions are threefold. First, we fine-tune a pre-trained Transformer to detect active sound classes. Second, we utilize a separate instance of this fine-tuned Transformer to perform sound event detection (SED), providing the separation module with detailed, time-varying guidance. Third, we implement an iterative refinement mechanism that progressively enhances separation quality by recursively reusing the separator's output from previous iterations. These advancements lead to significant improvements in both audio tagging and source separation performance, as demonstrated by our system's second-place finish in Task 4 of the DCASE Challenge 2025. Our implementation and model checkpoints are available in our GitHub repository: https://github.com/theMoro/dcase25task4 .", "date": "2025-07-23", "categories": []}
{"id": "2501.06959", "title": "Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music", "abstract": "Music source separation demixes a piece of music into its individual sound sources (vocals, percussion, melodic instruments, etc.), a task with no simple mathematical solution. It requires deep learning methods involving training on large datasets of isolated music stems. The most commonly available datasets are made from commercial Western music, limiting the models' applications to non-Western genres like Carnatic music. Carnatic music is a live tradition, with the available multi-track recordings containing overlapping sounds and bleeds between the sources. This poses a challenge to commercially available source separation models like Spleeter and Hybrid Demucs. In this work, we introduce 'Sanidha', the first open-source novel dataset for Carnatic music, offering studio-quality, multi-track recordings with minimal to no overlap or bleed. Along with the audio files, we provide high-definition videos of the artists' performances. Additionally, we fine-tuned Spleeter, one of the most commonly used source separation models, on our dataset and observed improved SDR performance compared to fine-tuning on a pre-existing Carnatic multi-track dataset. The outputs of the fine-tuned model with 'Sanidha' are evaluated through a listening study.", "date": "2025-01-12", "categories": []}
{"id": "2302.09409", "title": "LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy", "abstract": "Accurate sound source localization (SSL), such as direction-of-arrival (DoA) estimation, relies on consistent multichannel data. However, batteryless systems often suffer from missing data due to the stochastic nature of energy harvesting, degrading localization performance. We propose LOCUS, a deep learning framework that recovers corrupted features in such settings. LOCUS integrates three modules: (1) Information-Weighted Focus (InFo) to identify corrupted regions, (2) Latent Feature Synthesizer (LaFS) to reconstruct missing features, and (3) Guided Replacement (GRep) to restore data without altering valid inputs. LOCUS significantly improves DoA accuracy under missing-channel conditions, achieving up to 36.91% error reduction on DCASE and LargeSet, and 25.87-59.46% gains in real-world deployments. We release a 50-hour multichannel dataset to support future research on localization under energy constraints. Our code and data are available at: https://bashlab.github.io/locus_project/", "date": "2023-02-18", "categories": []}
{"id": "2507.10534", "title": "WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling", "abstract": "Despite rapid progress in end-to-end AI music generation, AI-driven modeling of professional Digital Signal Processing (DSP) workflows remains challenging. In particular, while there is growing interest in neural black-box modeling of audio effect graphs (e.g. reverb, compression, equalization), AI-based approaches struggle to replicate the nuanced signal flow and parameter interactions used in professional workflows. Existing differentiable plugin approaches often diverge from real-world tools, exhibiting inferior performance relative to simplified neural controllers under equivalent computational constraints. We introduce WildFX, a pipeline containerized with Docker for generating multi-track audio mixing datasets with rich effect graphs, powered by a professional Digital Audio Workstation (DAW) backend. WildFX supports seamless integration of cross-platform commercial plugins or any plugins in the wild, in VST/VST3/LV2/CLAP formats, enabling structural complexity (e.g., sidechains, crossovers) and achieving efficient parallelized processing. A minimalist metadata interface simplifies project/plugin configuration. Experiments demonstrate the pipeline's validity through blind estimation of mixing graphs, plugin/gain parameters, and its ability to bridge AI research with practical DSP demands. The code is available on: https://github.com/IsaacYQH/WildFX.", "date": "2025-07-14", "categories": []}
{"id": "2507.12042", "title": "Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification", "abstract": "This paper presents the objective, dataset, baseline, and metrics of Task 3 of the DCASE2025 Challenge on sound event localization and detection (SELD). In previous editions, the challenge used four-channel audio formats of first-order Ambisonics (FOA) and microphone array. In contrast, this year's challenge investigates SELD with stereo audio data (termed stereo SELD). This change shifts the focus from more specialized 360{\\deg} audio and audiovisual scene analysis to more commonplace audio and media scenarios with limited field-of-view (FOV). Due to inherent angular ambiguities in stereo audio data, the task focuses on direction-of-arrival (DOA) estimation in the azimuth plane (left-right axis) along with distance estimation. The challenge remains divided into two tracks: audio-only and audiovisual, with the audiovisual track introducing a new sub-task of onscreen/offscreen event classification necessitated by the limited FOV. This challenge introduces the DCASE2025 Task3 Stereo SELD Dataset, whose stereo audio and perspective video clips are sampled and converted from the STARSS23 recordings. The baseline system is designed to process stereo audio and corresponding video frames as inputs. In addition to the typical SELD event classification and localization, it integrates onscreen/offscreen classification for the audiovisual track. The evaluation metrics have been modified to introduce an onscreen/offscreen accuracy metric, which assesses the models' ability to identify which sound sources are onscreen. In the experimental evaluation, the baseline system performs reasonably well with the stereo audio data.", "date": "2025-07-16", "categories": []}
{"id": "2507.10464", "title": "AudioMAE++: learning better masked audio representations with SwiGLU FFNs", "abstract": "Masked Autoencoders (MAEs) trained on audio spectrogram patches have emerged as a prominent approach for learning self-supervised audio representations. While several recent papers have evaluated key aspects of training MAEs on audio data, the majority of these approaches still leverage vanilla transformer building blocks, whereas the transformer community has seen steady integration of newer architectural advancements. In this work, we propose AudioMAE++, a revamped audio masked autoencoder with two such enhancements, namely macaron-style transformer blocks with gated linear units. When pretrained on the AudioSet dataset, the proposed AudioMAE++ models outperform existing MAE based approaches on 10 diverse downstream tasks, demonstrating excellent performance on audio classification and speech-based benchmarks. The proposed AudioMAE++ models also demonstrate excellent scaling characteristics, outperforming directly comparable standard MAE baselines with up to 4x more parameters.", "date": "2025-07-14", "categories": []}
{"id": "2507.07066", "title": "Latent Acoustic Mapping for Direction of Arrival Estimation: A Self-Supervised Approach", "abstract": "Acoustic mapping techniques have long been used in spatial audio processing for direction of arrival estimation (DoAE). Traditional beamforming methods for acoustic mapping, while interpretable, often rely on iterative solvers that can be computationally intensive and sensitive to acoustic variability. On the other hand, recent supervised deep learning approaches offer feedforward speed and robustness but require large labeled datasets and lack interpretability. Despite their strengths, both methods struggle to consistently generalize across diverse acoustic setups and array configurations, limiting their broader applicability. We introduce the Latent Acoustic Mapping (LAM) model, a self-supervised framework that bridges the interpretability of traditional methods with the adaptability and efficiency of deep learning methods. LAM generates high-resolution acoustic maps, adapts to varying acoustic conditions, and operates efficiently across different microphone arrays. We assess its robustness on DoAE using the LOCATA and STARSS benchmarks. LAM achieves comparable or superior localization performance to existing supervised methods. Additionally, we show that LAM's acoustic maps can serve as effective features for supervised models, further enhancing DoAE accuracy and underscoring its potential to advance adaptive, high-performance sound localization systems.", "date": "2025-07-08", "categories": []}
{"id": "2507.04230", "title": "High-Resolution Sustain Pedal Depth Estimation from Piano Audio Across Room Acoustics", "abstract": "Piano sustain pedal detection has previously been approached as a binary on/off classification task, limiting its application in real-world piano performance scenarios where pedal depth significantly influences musical expression. This paper presents a novel approach for high-resolution estimation that predicts continuous pedal depth values. We introduce a Transformer-based architecture that not only matches state-of-the-art performance on the traditional binary classification task but also achieves high accuracy in continuous pedal depth estimation. Furthermore, by estimating continuous values, our model provides musically meaningful predictions for sustain pedal usage, whereas baseline models struggle to capture such nuanced expressions with their binary detection approach. Additionally, this paper investigates the influence of room acoustics on sustain pedal estimation using a synthetic dataset that includes varied acoustic conditions. We train our model with different combinations of room settings and test it in an unseen new environment using a \"leave-one-out\" approach. Our findings show that the two baseline models and ours are not robust to unseen room conditions. Statistical analysis further confirms that reverberation influences model predictions and introduces an overestimation bias.", "date": "2025-07-06", "categories": []}
{"id": "2507.02109", "title": "Parametric Neural Amp Modeling with Active Learning", "abstract": "We introduce PANAMA, an active learning framework for the training of end-to-end parametric guitar amp models using a WaveNet-like architecture. With \\model, one can create a virtual amp by recording samples that are determined by an active learning strategy to use a minimum amount of datapoints (i.e., amp knob settings). We show that gradient-based optimization algorithms can be used to determine the optimal datapoints to sample, and that the approach helps under a constrained number of samples.", "date": "2025-07-02", "categories": []}
{"id": "2410.11062", "title": "CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel Pruning", "abstract": "This paper presents CleanUMamba, a time-domain neural network architecture designed for real-time causal audio denoising directly applied to raw waveforms. CleanUMamba leverages a U-Net encoder-decoder structure, incorporating the Mamba state-space model in the bottleneck layer. By replacing conventional self-attention and LSTM mechanisms with Mamba, our architecture offers superior denoising performance while maintaining a constant memory footprint, enabling streaming operation. To enhance efficiency, we applied structured channel pruning, achieving an 8X reduction in model size without compromising audio quality. Our model demonstrates strong results in the Interspeech 2020 Deep Noise Suppression challenge. Specifically, CleanUMamba achieves a PESQ score of 2.42 and STOI of 95.1% with only 442K parameters and 468M MACs, matching or outperforming larger models in real-time performance. Code will be available at: https://github.com/lab-emi/CleanUMamba", "date": "2024-10-14", "categories": []}
{"id": "2506.22237", "title": "Fine-Tuning MIDI-to-Audio Alignment using a Neural Network on Piano Roll and CQT Representations", "abstract": "In this paper, we present a neural network approach for synchronizing audio recordings of human piano performances with their corresponding loosely aligned MIDI files. The task is addressed using a Convolutional Recurrent Neural Network (CRNN) architecture, which effectively captures spectral and temporal features by processing an unaligned piano roll and a spectrogram as inputs to estimate the aligned piano roll. To train the network, we create a dataset of piano pieces with augmented MIDI files that simulate common human timing errors. The proposed model achieves up to 20% higher alignment accuracy than the industry-standard Dynamic Time Warping (DTW) method across various tolerance windows. Furthermore, integrating DTW with the CRNN yields additional improvements, offering enhanced robustness and consistency. These findings demonstrate the potential of neural networks in advancing state-of-the-art MIDI-to-audio alignment.", "date": "2025-06-27", "categories": []}
{"id": "2506.21440", "title": "Learnable Adaptive Time-Frequency Representation via Differentiable Short-Time Fourier Transform", "abstract": "The short-time Fourier transform (STFT) is widely used for analyzing non-stationary signals. However, its performance is highly sensitive to its parameters, and manual or heuristic tuning often yields suboptimal results. To overcome this limitation, we propose a unified differentiable formulation of the STFT that enables gradient-based optimization of its parameters. This approach addresses the limitations of traditional STFT parameter tuning methods, which often rely on computationally intensive discrete searches. It enables fine-tuning of the time-frequency representation (TFR) based on any desired criterion. Moreover, our approach integrates seamlessly with neural networks, allowing joint optimization of the STFT parameters and network weights. The efficacy of the proposed differentiable STFT in enhancing TFRs and improving performance in downstream tasks is demonstrated through experiments on both simulated and real-world data.", "date": "2025-06-26", "categories": []}
{"id": "2506.02858", "title": "DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization", "abstract": "Language-queried Audio Source Separation (LASS) enables open-vocabulary sound separation via natural language queries. While existing methods rely on task-specific training, we explore whether pretrained diffusion models, originally designed for audio generation, can inherently perform separation without further training. In this study, we introduce a training-free framework leveraging generative priors for zero-shot LASS. Analyzing naive adaptations, we identify key limitations arising from modality-specific challenges. To address these issues, we propose Diffusion-Guided Mask Optimization (DGMO), a test-time optimization framework that refines spectrogram masks for precise, input-aligned separation. Our approach effectively repurposes pretrained diffusion models for source separation, achieving competitive performance without task-specific supervision. This work expands the application of diffusion models beyond generation, establishing a new paradigm for zero-shot audio separation. The code is available at: https://wltschmrz.github.io/DGMO/", "date": "2025-06-03", "categories": []}
{"id": "2506.18954", "title": "SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer", "abstract": "This paper describes a sound source localization (SSL) technique that combines an $\\alpha$-stable model for the observed signal with a neural network-based approach for modeling steering vectors. Specifically, a physics-informed neural network, referred to as Neural Steerer, is used to interpolate measured steering vectors (SVs) on a fixed microphone array. This allows for a more robust estimation of the so-called $\\alpha$-stable spatial measure, which represents the most plausible direction of arrival (DOA) of a target signal. As an $\\alpha$-stable model for the non-Gaussian case ($\\alpha$ $\\in$ (0, 2)) theoretically defines a unique spatial measure, we choose to leverage it to account for residual reconstruction error of the Neural Steerer in the downstream tasks. The objective scores indicate that our proposed technique outperforms state-of-the-art methods in the case of multiple sound sources.", "date": "2025-06-23", "categories": []}
{"id": "2505.05657", "title": "ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior", "abstract": "Blind Speech Separation (BSS) aims to separate multiple speech sources from audio mixtures recorded by a microphone array. The problem is challenging because it is a blind inverse problem, i.e., the microphone array geometry, the room impulse response (RIR), and the speech sources, are all unknown. We propose ArrayDPS to solve the BSS problem in an unsupervised, array-agnostic, and generative manner. The core idea builds on diffusion posterior sampling (DPS), but unlike DPS where the likelihood is tractable, ArrayDPS must approximate the likelihood by formulating a separate optimization problem. The solution to the optimization approximates room acoustics and the relative transfer functions between microphones. These approximations, along with the diffusion priors, iterate through the ArrayDPS sampling process and ultimately yield separated voice sources. We only need a simple single-speaker speech diffusion model as a prior along with the mixtures recorded at the microphones; no microphone array information is necessary. Evaluation results show that ArrayDPS outperforms all baseline unsupervised methods while being comparable to supervised methods in terms of SDR. Audio demos are provided at: https://arraydps.github.io/ArrayDPSDemo/.", "date": "2025-05-08", "categories": []}
{"id": "2409.03377", "title": "aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio", "abstract": "We present aTENNuate, a simple deep state-space autoencoder configured for efficient online raw speech enhancement in an end-to-end fashion. The network's performance is primarily evaluated on raw speech denoising, with additional assessments on tasks such as super-resolution and de-quantization. We benchmark aTENNuate on the VoiceBank + DEMAND and the Microsoft DNS1 synthetic test sets. The network outperforms previous real-time denoising models in terms of PESQ score, parameter count, MACs, and latency. Even as a raw waveform processing model, the model maintains high fidelity to the clean signal with minimal audible artifacts. In addition, the model remains performant even when the noisy input is compressed down to 4000Hz and 4 bits, suggesting general speech enhancement capabilities in low-resource environments. Try it out by pip install attenuate", "date": "2024-09-05", "categories": []}
{"id": "2506.09792", "title": "Incorporating Linguistic Constraints from External Knowledge Source for Audio-Visual Target Speech Extraction", "abstract": "Audio-visual target speaker extraction (AV-TSE) models primarily rely on target visual cues to isolate the target speaker's voice from others. We know that humans leverage linguistic knowledge, such as syntax and semantics, to support speech perception. Inspired by this, we explore the potential of pre-trained speech-language models (PSLMs) and pre-trained language models (PLMs) as auxiliary knowledge sources for AV-TSE. In this study, we propose incorporating the linguistic constraints from PSLMs or PLMs for the AV-TSE model as additional supervision signals. Without introducing any extra computational cost during inference, the proposed approach consistently improves speech quality and intelligibility. Furthermore, we evaluate our method in multi-language settings and visual cue-impaired scenarios and show robust performance gains.", "date": "2025-06-11", "categories": []}
{"id": "2506.12039", "title": "The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks", "abstract": "We present the Maximal Overlap Discrete Wavelet Scattering Transform (MODWST), whose construction is inspired by the combination of the Maximal Overlap Discrete Wavelet Transform (MODWT) and the Scattering Wavelet Transform (WST). We also discuss the use of MODWST in classification tasks, evaluating its performance in two applications: stationary signal classification and ECG signal classification. The results demonstrate that MODWST achieved good performance in both applications, positioning itself as a viable alternative to popular methods like Convolutional Neural Networks (CNNs), particularly when the training data set is limited.", "date": "2025-05-23", "categories": []}
{"id": "2506.12199", "title": "ViSAGe: Video-to-Spatial Audio Generation", "abstract": "Spatial audio is essential for enhancing the immersiveness of audio-visual experiences, yet its production typically demands complex recording systems and specialized expertise. In this work, we address a novel problem of generating first-order ambisonics, a widely used spatial audio format, directly from silent videos. To support this task, we introduce YT-Ambigen, a dataset comprising 102K 5-second YouTube video clips paired with corresponding first-order ambisonics. We also propose new evaluation metrics to assess the spatial aspect of generated audio based on audio energy maps and saliency metrics. Furthermore, we present Video-to-Spatial Audio Generation (ViSAGe), an end-to-end framework that generates first-order ambisonics from silent video frames by leveraging CLIP visual features, autoregressive neural audio codec modeling with both directional and visual guidance. Experimental results demonstrate that ViSAGe produces plausible and coherent first-order ambisonics, outperforming two-stage approaches consisting of video-to-audio generation and audio spatialization. Qualitative examples further illustrate that ViSAGe generates temporally aligned high-quality spatial audio that adapts to viewpoint changes.", "date": "2025-06-13", "categories": []}
{"id": "2310.03480", "title": "The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids", "abstract": "This paper reports on the design and results of the 2024 ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids. The Cadenza project is working to enhance the audio quality of music for those with a hearing loss. The scenario for the challenge was listening to stereo reproduction over loudspeakers via hearing aids. The task was to: decompose pop/rock music into vocal, drums, bass and other (VDBO); rebalance the different tracks with specified gains and then remixing back to stereo. End-to-end approaches were also accepted. 17 systems were submitted by 11 teams. Causal systems performed poorer than non-causal approaches. 9 systems beat the baseline. A common approach was to fine-tuning pretrained demixing models. The best approach used an ensemble of models.", "date": "2023-10-05", "categories": []}
{"id": "2503.16862", "title": "Improving Acoustic Scene Classification with City Features", "abstract": "Acoustic scene recordings are often collected from a diverse range of cities. Most existing acoustic scene classification (ASC) approaches focus on identifying common acoustic scene patterns across cities to enhance generalization. However, the potential acoustic differences introduced by city-specific environmental and cultural factors are overlooked. In this paper, we hypothesize that the city-specific acoustic features are beneficial for the ASC task rather than being treated as noise or bias. To this end, we propose City2Scene, a novel framework that leverages city features to improve ASC. Unlike conventional approaches that may discard or suppress city information, City2Scene transfers the city-specific knowledge from pre-trained city classification models to scene classification model using knowledge distillation. We evaluate City2Scene on three datasets of DCASE Challenge Task 1, which include both scene and city labels. Experimental results demonstrate that city features provide valuable information for classifying scenes. By distilling city-specific knowledge, City2Scene effectively improves accuracy across a variety of lightweight CNN backbones, achieving competitive performance to the top-ranked solutions of DCASE Challenge in recent years.", "date": "2025-03-21", "categories": []}
{"id": "2503.11373", "title": "Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models", "abstract": "We target the problem of developing new low-complexity networks for the sound event detection task. Our goal is to meticulously analyze the performance-complexity trade-off, aiming to be competitive with the large state-of-the-art models, at a fraction of the computational requirements. We find that low-complexity convolutional models previously proposed for audio tagging can be effectively adapted for event detection (which requires frame-wise prediction) by adjusting convolutional strides, removing the global pooling, and, importantly, adding a sequence model before the (now frame-wise) classification heads. Systematic experiments reveal that the best choice for the sequence model type depends on which complexity metric is most important for the given application. We also investigate the impact of enhanced training strategies such as knowledge distillation. In the end, we show that combined with an optimized training strategy, we can reach event detection performance comparable to state-of-the-art transformers while requiring only around 5% of the parameters. We release all our pre-trained models and the code for reproducing this work to support future research in low-complexity sound event detection at https://github.com/theMoro/EfficientSED.", "date": "2025-03-14", "categories": []}
{"id": "2402.03710", "title": "Listen, Chat, and Remix: Text-Guided Soundscape Remixing for Enhanced Auditory Experience", "abstract": "In daily life, we encounter a variety of sounds, both desirable and undesirable, with limited control over their presence and volume. Our work introduces \"Listen, Chat, and Remix\" (LCR), a novel multimodal sound remixer that controls each sound source in a mixture based on user-provided text instructions. LCR distinguishes itself with a user-friendly text interface and its unique ability to remix multiple sound sources simultaneously within a mixture, without needing to separate them. Users input open-vocabulary text prompts, which are interpreted by a large language model to create a semantic filter for remixing the sound mixture. The system then decomposes the mixture into its components, applies the semantic filter, and reassembles filtered components back to the desired output. We developed a 160-hour dataset with over 100k mixtures, including speech and various audio sources, along with text prompts for diverse remixing tasks including extraction, removal, and volume control of single or multiple sources. Our experiments demonstrate significant improvements in signal quality across all remixing tasks and robust performance in zero-shot scenarios with varying numbers and types of sound sources. An audio demo is available at: https://listenchatremix.github.io/demo.", "date": "2024-02-06", "categories": []}
{"id": "2410.16785", "title": "Annotation-Free MIDI-to-Audio Synthesis via Concatenative Synthesis and Generative Refinement", "abstract": "Recent MIDI-to-audio synthesis methods using deep neural networks have successfully generated high-quality, expressive instrumental tracks. However, these methods require MIDI annotations for supervised training, limiting the diversity of instrument timbres and expression styles in the output. We propose CoSaRef, a MIDI-to-audio synthesis method that does not require MIDI-audio paired datasets. CoSaRef first generates a synthetic audio track using concatenative synthesis based on MIDI input, then refines it with a diffusion-based deep generative model trained on datasets without MIDI annotations. This approach improves the diversity of timbres and expression styles. Additionally, it allows detailed control over timbres and expression through audio sample selection and extra MIDI design, similar to traditional functions in digital audio workstations. Experiments showed that CoSaRef could generate realistic tracks while preserving fine-grained timbre control via one-shot samples. Moreover, despite not being supervised on MIDI annotation, CoSaRef outperformed the state-of-the-art timbre-controllable method based on MIDI supervision in both objective and subjective evaluation.", "date": "2024-10-22", "categories": []}
{"id": "2506.07199", "title": "Audio synthesizer inversion in symmetric parameter spaces with approximately equivariant flow matching", "abstract": "Many audio synthesizers can produce the same signal given different parameter configurations, meaning the inversion from sound to parameters is an inherently ill-posed problem. We show that this is largely due to intrinsic symmetries of the synthesizer, and focus in particular on permutation invariance. First, we demonstrate on a synthetic task that regressing point estimates under permutation symmetry degrades performance, even when using a permutation-invariant loss function or symmetry-breaking heuristics. Then, viewing equivalent solutions as modes of a probability distribution, we show that a conditional generative model substantially improves performance. Further, acknowledging the invariance of the implicit parameter distribution, we find that performance is further improved by using a permutation equivariant continuous normalizing flow. To accommodate intricate symmetries in real synthesizers, we also propose a relaxed equivariance strategy that adaptively discovers relevant symmetries from data. Applying our method to Surge XT, a full-featured open source synthesizer used in real world audio production, we find our method outperforms regression and generative baselines across audio reconstruction metrics.", "date": "2025-06-08", "categories": []}
{"id": "2506.05593", "title": "Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling", "abstract": "In recent years, end-to-end approaches have made notable progress in addressing the challenge of speaker diarization, which involves segmenting and identifying speakers in multi-talker recordings. One such approach, Encoder-Decoder Attractors (EDA), has been proposed to handle variable speaker counts as well as better guide the network during training. In this study, we extend the attractor paradigm by moving beyond direct speaker modeling and instead focus on representing more detailed `speaker attributes' through a multi-stage process of intermediate representations. Additionally, we enhance the architecture by replacing transformers with conformers, a convolution-augmented transformer, to model local dependencies. Experiments demonstrate improved diarization performance on the CALLHOME dataset.", "date": "2025-06-05", "categories": []}
{"id": "2503.07352", "title": "Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical Music", "abstract": "Music source separation is the task of separating a mixture of instruments into constituent tracks. Music source separation models are typically trained using only audio data, although additional information can be used to improve the model's separation capability. In this paper, we propose two ways of using musical scores to aid music source separation: a score-informed model where the score is concatenated with the magnitude spectrogram of the audio mixture as the input of the model, and a model where we use only the score to calculate the separation mask. We train our models on synthetic data in the SynthSOD dataset and evaluate our methods on the URMP and Aalto anechoic orchestra datasets, comprised of real recordings. The score-informed model improves separation results compared to a baseline approach, but struggles to generalize from synthetic to real data, whereas the score-only model shows a clear improvement in synthetic-to-real generalization.", "date": "2025-03-10", "categories": []}
{"id": "2504.14906", "title": "OmniAudio: Generating Spatial Audio from 360-Degree Video", "abstract": "Traditional video-to-audio generation techniques primarily focus on perspective video and non-spatial audio, often missing the spatial cues necessary for accurately representing sound sources in 3D environments. To address this limitation, we introduce a novel task, 360V2SA, to generate spatial audio from 360-degree videos, specifically producing First-order Ambisonics (FOA) audio - a standard format for representing 3D spatial audio that captures sound directionality and enables realistic 3D audio reproduction. We first create Sphere360, a novel dataset tailored for this task that is curated from real-world data. We also design an efficient semi-automated pipeline for collecting and cleaning paired video-audio data. To generate spatial audio from 360-degree video, we propose a novel framework OmniAudio, which leverages self-supervised pre-training using both spatial audio data (in FOA format) and large-scale non-spatial data. Furthermore, OmniAudio features a dual-branch framework that utilizes both panoramic and perspective video inputs to capture comprehensive local and global information from 360-degree videos. Experimental results demonstrate that OmniAudio achieves state-of-the-art performance across both objective and subjective metrics on Sphere360. Code and datasets are available at https://github.com/liuhuadai/OmniAudio. The project website is available at https://OmniAudio-360V2SA.github.io.", "date": "2025-04-21", "categories": []}
{"id": "2506.00273", "title": "SoundSculpt: Direction and Semantics Driven Ambisonic Target Sound Extraction", "abstract": "This paper introduces SoundSculpt, a neural network designed to extract target sound fields from ambisonic recordings. SoundSculpt employs an ambisonic-in-ambisonic-out architecture and is conditioned on both spatial information (e.g., target direction obtained by pointing at an immersive video) and semantic embeddings (e.g., derived from image segmentation and captioning). Trained and evaluated on synthetic and real ambisonic mixtures, SoundSculpt demonstrates superior performance compared to various signal processing baselines. Our results further reveal that while spatial conditioning alone can be effective, the combination of spatial and semantic information is beneficial in scenarios where there are secondary sound sources spatially close to the target. Additionally, we compare two different semantic embeddings derived from a text description of the target sound using text encoders.", "date": "2025-05-30", "categories": []}
{"id": "2506.00385", "title": "MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation", "abstract": "Neural audio codecs have made significant strides in efficiently mapping raw audio waveforms into discrete token representations, which are foundational for contemporary audio generative models. However, most existing codecs are optimized primarily for reconstruction quality, often at the expense of the downstream modelability of the encoded tokens. Motivated by the need to overcome this bottleneck, we introduce $\\textbf{MagiCodec}$, a novel single-layer, streaming Transformer-based audio codec. MagiCodec is designed with a multistage training pipeline that incorporates Gaussian noise injection and latent regularization, explicitly targeting the enhancement of semantic expressiveness in the generated codes while preserving high reconstruction fidelity. We analytically derive the effect of noise injection in the frequency domain, demonstrating its efficacy in attenuating high-frequency components and fostering robust tokenization. Extensive experimental evaluations show that MagiCodec surpasses state-of-the-art codecs in both reconstruction quality and downstream tasks. Notably, the tokens produced by MagiCodec exhibit Zipf-like distributions, as observed in natural languages, thereby improving compatibility with language-model-based generative architectures. The code and pre-trained models are available at https://github.com/Ereboas/MagiCodec.", "date": "2025-05-31", "categories": []}
{"id": "2506.00927", "title": "In-the-wild Audio Spatialization with Flexible Text-guided Localization", "abstract": "To enhance immersive experiences, binaural audio offers spatial awareness of sounding objects in AR, VR, and embodied AI applications. While existing audio spatialization methods can generally map any available monaural audio to binaural audio signals, they often lack the flexible and interactive control needed in complex multi-object user-interactive environments. To address this, we propose a Text-guided Audio Spatialization (TAS) framework that utilizes flexible text prompts and evaluates our model from unified generation and comprehension perspectives. Due to the limited availability of premium and large-scale stereo data, we construct the SpatialTAS dataset, which encompasses 376,000 simulated binaural audio samples to facilitate the training of our model. Our model learns binaural differences guided by 3D spatial location and relative position prompts, augmented by flipped-channel audio. It outperforms existing methods on both simulated and real-recorded datasets, demonstrating superior generalization and accuracy. Besides, we develop an assessment model based on Llama-3.1-8B, which evaluates the spatial semantic coherence between our generated binaural audio and text prompts through a spatial reasoning task. Results demonstrate that text prompts provide flexible and interactive control to generate binaural audio with excellent quality and semantic consistency in spatial locations. Dataset is available at \\href{https://github.com/Alice01010101/TASU}", "date": "2025-06-01", "categories": []}
{"id": "2505.23964", "title": "Acoustic Classification of Maritime Vessels using Learnable Filterbanks", "abstract": "Reliably monitoring and recognizing maritime vessels based on acoustic signatures is complicated by the variability of different recording scenarios. A robust classification framework must be able to generalize across diverse acoustic environments and variable source-sensor distances. To this end, we present a deep learning model with robust performance across different recording scenarios. Using a trainable spectral front-end and temporal feature encoder to learn a Gabor filterbank, the model can dynamically emphasize different frequency components. Trained on the VTUAD hydrophone recordings from the Strait of Georgia, our model, CATFISH, achieves a state-of-the-art 96.63 % percent test accuracy across varying source-sensor distances, surpassing the previous benchmark by over 12 percentage points. We present the model, justify our architectural choices, analyze the learned Gabor filters, and perform ablation studies on sensor data fusion and attention-based pooling.", "date": "2025-05-29", "categories": []}
{"id": "2505.22865", "title": "BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models", "abstract": "Binaural rendering aims to synthesize binaural audio that mimics natural hearing based on a mono audio and the locations of the speaker and listener. Although many methods have been proposed to solve this problem, they struggle with rendering quality and streamable inference. Synthesizing high-quality binaural audio that is indistinguishable from real-world recordings requires precise modeling of binaural cues, room reverb, and ambient sounds. Additionally, real-world applications demand streaming inference. To address these challenges, we propose a flow matching based streaming binaural speech synthesis framework called BinauralFlow. We consider binaural rendering to be a generation problem rather than a regression problem and design a conditional flow matching model to render high-quality audio. Moreover, we design a causal U-Net architecture that estimates the current audio frame solely based on past information to tailor generative models for streaming inference. Finally, we introduce a continuous inference pipeline incorporating streaming STFT/ISTFT operations, a buffer bank, a midpoint solver, and an early skip schedule to improve rendering continuity and speed. Quantitative and qualitative evaluations demonstrate the superiority of our method over SOTA approaches. A perceptual study further reveals that our model is nearly indistinguishable from real-world recordings, with a $42\\%$ confusion rate.", "date": "2025-05-28", "categories": []}
{"id": "2505.23625", "title": "ZeroSep: Separate Anything in Audio with Zero Training", "abstract": "Audio source separation is fundamental for machines to understand complex acoustic environments and underpins numerous audio applications. Current supervised deep learning approaches, while powerful, are limited by the need for extensive, task-specific labeled data and struggle to generalize to the immense variability and open-set nature of real-world acoustic scenes. Inspired by the success of generative foundation models, we investigate whether pre-trained text-guided audio diffusion models can overcome these limitations. We make a surprising discovery: zero-shot source separation can be achieved purely through a pre-trained text-guided audio diffusion model under the right configuration. Our method, named ZeroSep, works by inverting the mixed audio into the diffusion model's latent space and then using text conditioning to guide the denoising process to recover individual sources. Without any task-specific training or fine-tuning, ZeroSep repurposes the generative diffusion model for a discriminative separation task and inherently supports open-set scenarios through its rich textual priors. ZeroSep is compatible with a variety of pre-trained text-guided audio diffusion backbones and delivers strong separation performance on multiple separation benchmarks, surpassing even supervised methods.", "date": "2025-05-29", "categories": []}
{"id": "2412.08356", "title": "Zero-Shot Mono-to-Binaural Speech Synthesis", "abstract": "We present ZeroBAS, a neural method to synthesize binaural audio from monaural audio recordings and positional information without training on any binaural data. To our knowledge, this is the first published zero-shot neural approach to mono-to-binaural audio synthesis. Specifically, we show that a parameter-free geometric time warping and amplitude scaling based on source location suffices to get an initial binaural synthesis that can be refined by iteratively applying a pretrained denoising vocoder. Furthermore, we find this leads to generalization across room conditions, which we measure by introducing a new dataset, TUT Mono-to-Binaural, to evaluate state-of-the-art monaural-to-binaural synthesis methods on unseen conditions. Our zero-shot method is perceptually on-par with the performance of supervised methods on the standard mono-to-binaural dataset, and even surpasses them on our out-of-distribution TUT Mono-to-Binaural dataset. Our results highlight the potential of pretrained generative audio models and zero-shot learning to unlock robust binaural audio synthesis.", "date": "2024-12-11", "categories": []}
{"id": "2505.21827", "title": "Music Source Restoration", "abstract": "We introduce Music Source Restoration (MSR), a novel task addressing the gap between idealized source separation and real-world music production. Current Music Source Separation (MSS) approaches assume mixtures are simple sums of sources, ignoring signal degradations employed during music production like equalization, compression, and reverb. MSR models mixtures as degraded sums of individually degraded sources, with the goal of recovering original, undegraded signals. Due to the lack of data for MSR, we present RawStems, a dataset annotation of 578 songs with unprocessed source signals organized into 8 primary and 17 secondary instrument groups, totaling 354.13 hours. To the best of our knowledge, RawStems is the first dataset that contains unprocessed music stems with hierarchical categories. We consider spectral filtering, dynamic range compression, harmonic distortion, reverb and lossy codec as possible degradations, and establish U-Former as a baseline method, demonstrating the feasibility of MSR on our dataset. We release the RawStems dataset annotations, degradation simulation pipeline, training code and pre-trained models to be publicly available.", "date": "2025-05-27", "categories": []}
{"id": "2501.18470", "title": "Resampling Filter Design for Multirate Neural Audio Effect Processing", "abstract": "Neural networks have become ubiquitous in audio effects modelling, especially for guitar amplifiers and distortion pedals. One limitation of such models is that the sample rate of the training data is implicitly encoded in the model weights and therefore not readily adjustable at inference. Recent work explored modifications to recurrent neural network architecture to approximate a sample rate independent system, enabling audio processing at a rate that differs from the original training rate. This method works well for integer oversampling and can reduce aliasing caused by nonlinear activation functions. For small fractional changes in sample rate, fractional delay filters can be used to approximate sample rate independence, but in some cases this method fails entirely. Here, we explore the use of real-time signal resampling at the input and output of the neural network as an alternative solution. We investigate several resampling filter designs and show that a two-stage design consisting of a half-band IIR filter cascaded with a Kaiser window FIR filter can give similar or better results to the previously proposed model adjustment method with many fewer filtering operations per sample and less than one millisecond of latency at typical audio rates. Furthermore, we investigate interpolation and decimation filters for the task of integer oversampling and show that cascaded half-band IIR and FIR designs can be used in conjunction with the model adjustment method to reduce aliasing in a range of distortion effect models.", "date": "2025-01-30", "categories": []}
{"id": "2505.20635", "title": "Plug-and-Play Co-Occurring Face Attention for Robust Audio-Visual Speaker Extraction", "abstract": "Audio-visual speaker extraction isolates a target speaker's speech from a mixture speech signal conditioned on a visual cue, typically using the target speaker's face recording. However, in real-world scenarios, other co-occurring faces are often present on-screen, providing valuable speaker activity cues in the scene. In this work, we introduce a plug-and-play inter-speaker attention module to process these flexible numbers of co-occurring faces, allowing for more accurate speaker extraction in complex multi-person environments. We integrate our module into two prominent models: the AV-DPRNN and the state-of-the-art AV-TFGridNet. Extensive experiments on diverse datasets, including the highly overlapped VoxCeleb2 and sparsely overlapped MISP, demonstrate that our approach consistently outperforms baselines. Furthermore, cross-dataset evaluations on LRS2 and LRS3 confirm the robustness and generalizability of our method.", "date": "2025-05-27", "categories": []}
{"id": "2505.20961", "title": "Efficient and Microphone-Fault-Tolerant 3D Sound Source Localization", "abstract": "Sound source localization (SSL) is a critical technology for determining the position of sound sources in complex environments. However, existing methods face challenges such as high computational costs and precise calibration requirements, limiting their deployment in dynamic or resource-constrained environments. This paper introduces a novel 3D SSL framework, which uses sparse cross-attention, pretraining, and adaptive signal coherence metrics, to achieve accurate and computationally efficient localization with fewer input microphones. The framework is also fault-tolerant to unreliable or even unknown microphone position inputs, ensuring its applicability in real-world scenarios. Preliminary experiments demonstrate its scalability for multi-source localization without requiring additional hardware. This work advances SSL by balancing the model's performance and efficiency and improving its robustness for real-world scenarios.", "date": "2025-05-27", "categories": []}
{"id": "2502.01185", "title": "Deep Active Speech Cancellation with Mamba-Masking Network", "abstract": "We present a novel deep learning network for Active Speech Cancellation (ASC), advancing beyond Active Noise Cancellation (ANC) methods by effectively canceling both noise and speech signals. The proposed Mamba-Masking architecture introduces a masking mechanism that directly interacts with the encoded reference signal, enabling adaptive and precisely aligned anti-signal generation-even under rapidly changing, high-frequency conditions, as commonly found in speech. Complementing this, a multi-band segmentation strategy further improves phase alignment across frequency bands. Additionally, we introduce an optimization-driven loss function that provides near-optimal supervisory signals for anti-signal generation. Experimental results demonstrate substantial performance gains, achieving up to 7.2dB improvement in ANC scenarios and 6.2dB in ASC, significantly outperforming existing methods.", "date": "2025-02-03", "categories": []}
{"id": "2505.03273", "title": "SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation", "abstract": "While contemporary speech separation technologies adeptly process lengthy mixed audio waveforms, they are frequently challenged by the intricacies of real-world environments, including noisy and reverberant settings, which can result in artifacts or distortions in the separated speech. To overcome these limitations, we introduce SepALM, a pioneering approach that employs audio language models (ALMs) to rectify and re-synthesize speech within the text domain following preliminary separation. SepALM comprises four core components: a separator, a corrector, a synthesizer, and an aligner. By integrating an ALM-based end-to-end error correction mechanism, we mitigate the risk of error accumulation and circumvent the optimization hurdles typically encountered in conventional methods that amalgamate automatic speech recognition (ASR) with large language models (LLMs). Additionally, we have developed Chain-of-Thought (CoT) prompting and knowledge distillation techniques to facilitate the reasoning and training processes of the ALM. Our experiments substantiate that SepALM not only elevates the precision of speech separation but also markedly bolsters adaptability in novel acoustic environments.", "date": "2025-05-06", "categories": []}
{"id": "2505.19534", "title": "Training-Free Multi-Step Audio Source Separation", "abstract": "Audio source separation aims to separate a mixture into target sources. Previous audio source separation systems usually conduct one-step inference, which does not fully explore the separation ability of models. In this work, we reveal that pretrained one-step audio source separation models can be leveraged for multi-step separation without additional training. We propose a simple yet effective inference method that iteratively applies separation by optimally blending the input mixture with the previous step's separation result. At each step, we determine the optimal blending ratio by maximizing a metric. We prove that our method always yield improvement over one-step inference, provide error bounds based on model smoothness and metric robustness, and provide theoretical analysis connecting our method to denoising along linear interpolation paths between noise and clean distributions, a property we link to denoising diffusion bridge models. Our approach effectively delivers improved separation performance as a \"free lunch\" from existing models. Our empirical results demonstrate that our multi-step separation approach consistently outperforms one-step inference across both speech enhancement and music source separation tasks, and can achieve scaling performance similar to training a larger model, using more data, or in some cases employing a multi-step training objective. These improvements appear not only on the optimization metric during multi-step inference, but also extend to nearly all non-optimized metrics (with one exception). We also discuss limitations of our approach and directions for future research.", "date": "2025-05-26", "categories": []}
{"id": "2505.16911", "title": "Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation", "abstract": "We introduce a new paradigm for active sound modification: Active Speech Enhancement (ASE). While Active Noise Cancellation (ANC) algorithms focus on suppressing external interference, ASE goes further by actively shaping the speech signal -- both attenuating unwanted noise components and amplifying speech-relevant frequencies -- to improve intelligibility and perceptual quality. To enable this, we propose a novel Transformer-Mamba-based architecture, along with a task-specific loss function designed to jointly optimize interference suppression and signal enrichment. Our method outperforms existing baselines across multiple speech processing tasks -- including denoising, dereverberation, and declipping -- demonstrating the effectiveness of active, targeted modulation in challenging acoustic environments.", "date": "2025-05-22", "categories": []}
{"id": "2505.17823", "title": "Source Separation of Small Classical Ensembles: Challenges and Opportunities", "abstract": "Musical (MSS) source separation of western popular music using non-causal deep learning can be very effective. In contrast, MSS for classical music is an unsolved problem. Classical ensembles are harder to separate than popular music because of issues such as the inherent greater variation in the music; the sparsity of recordings with ground truth for supervised training; and greater ambiguity between instruments. The Cadenza project has been exploring MSS for classical music. This is being done so music can be remixed to improve listening experiences for people with hearing loss. To enable the work, a new database of synthesized woodwind ensembles was created to overcome instrumental imbalances in the EnsembleSet. For the MSS, a set of ConvTasNet models was used with each model being trained to extract a string or woodwind instrument. ConvTasNet was chosen because it enabled both causal and non-causal approaches to be tested. Non-causal approaches have dominated MSS work and are useful for recorded music, but for live music or processing on hearing aids, causal signal processing is needed. The MSS performance was evaluated on the two small datasets (Bach10 and URMP) of real instrument recordings where the ground-truth is available. The performances of the causal and non-causal systems were similar. Comparing the average Signal-to-Distortion (SDR) of the synthesized validation set (6.2 dB causal; 6.9 non-causal), to the real recorded evaluation set (0.3 dB causal, 0.4 dB non-causal), shows that mismatch between synthesized and recorded data is a problem. Future work needs to either gather more real recordings that can be used for training, or to improve the realism and diversity of the synthesized recordings to reduce the mismatch...", "date": "2025-05-23", "categories": []}
{"id": "2505.17877", "title": "Toward Optimal ANC: Establishing Mutual Information Lower Bound", "abstract": "Active Noise Cancellation (ANC) algorithms aim to suppress unwanted acoustic disturbances by generating anti-noise signals that destructively interfere with the original noise in real time. Although recent deep learning-based ANC algorithms have set new performance benchmarks, there remains a shortage of theoretical limits to rigorously assess their improvements. To address this, we derive a unified lower bound on cancellation performance composed of two components. The first component is information-theoretic: it links residual error power to the fraction of disturbance entropy captured by the anti-noise signal, thereby quantifying limits imposed by information-processing capacity. The second component is support-based: it measures the irreducible error arising in frequency bands that the cancellation path cannot address, reflecting fundamental physical constraints. By taking the maximum of these two terms, our bound establishes a theoretical ceiling on the Normalized Mean Squared Error (NMSE) attainable by any ANC algorithm. We validate its tightness empirically on the NOISEX dataset under varying reverberation times, demonstrating robustness across diverse acoustic conditions.", "date": "2025-05-23", "categories": []}
{"id": "2505.13617", "title": "Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses", "abstract": "The characteristics of a sound field are intrinsically linked to the geometric and spatial properties of the environment surrounding a sound source and a listener. The physics of sound propagation is captured in a time-domain signal known as a room impulse response (RIR). Prior work using neural fields (NFs) has allowed learning spatially-continuous representations of RIRs from finite RIR measurements. However, previous NF-based methods have focused on monaural omnidirectional or at most binaural listeners, which does not precisely capture the directional characteristics of a real sound field at a single point. We propose a direction-aware neural field (DANF) that more explicitly incorporates the directional information by Ambisonic-format RIRs. While DANF inherently captures spatial relations between sources and listeners, we further propose a direction-aware loss. In addition, we investigate the ability of DANF to adapt to new rooms in various ways including low-rank adaptation.", "date": "2025-05-19", "categories": []}
{"id": "2505.14285", "title": "AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis", "abstract": "This paper presents AquaSignal, a modular and scalable pipeline for preprocessing, denoising, classification, and novelty detection of underwater acoustic signals. Designed to operate effectively in noisy and dynamic marine environments, AquaSignal integrates state-of-the-art deep learning architectures to enhance the reliability and accuracy of acoustic signal analysis. The system is evaluated on a combined dataset from the Deepship and Ocean Networks Canada (ONC) benchmarks, providing a diverse set of real-world underwater scenarios. AquaSignal employs a U-Net architecture for denoising, a ResNet18 convolutional neural network for classifying known acoustic events, and an AutoEncoder-based model for unsupervised detection of novel or anomalous signals. To our knowledge, this is the first comprehensive study to apply and evaluate this combination of techniques on maritime vessel acoustic data. Experimental results show that AquaSignal improves signal clarity and task performance, achieving 71% classification accuracy and 91% accuracy in novelty detection. Despite slightly lower classification performance compared to some state-of-the-art models, differences in data partitioning strategies limit direct comparisons. Overall, AquaSignal demonstrates strong potential for real-time underwater acoustic monitoring in scientific, environmental, and maritime domains.", "date": "2025-05-20", "categories": []}
{"id": "2505.14465", "title": "FlowTSE: Target Speaker Extraction with Flow Matching", "abstract": "Target speaker extraction (TSE) aims to isolate a specific speaker's speech from a mixture using speaker enrollment as a reference. While most existing approaches are discriminative, recent generative methods for TSE achieve strong results. However, generative methods for TSE remain underexplored, with most existing approaches relying on complex pipelines and pretrained components, leading to computational overhead. In this work, we present FlowTSE, a simple yet effective TSE approach based on conditional flow matching. Our model receives an enrollment audio sample and a mixed speech signal, both represented as mel-spectrograms, with the objective of extracting the target speaker's clean speech. Furthermore, for tasks where phase reconstruction is crucial, we propose a novel vocoder conditioned on the complex STFT of the mixed signal, enabling improved phase estimation. Experimental results on standard TSE benchmarks show that FlowTSE matches or outperforms strong baselines.", "date": "2025-05-20", "categories": []}
{"id": "2505.14517", "title": "Steering Deep Non-Linear Spatially Selective Filters for Weakly Guided Extraction of Moving Speakers in Dynamic Scenarios", "abstract": "Recent speaker extraction methods using deep non-linear spatial filtering perform exceptionally well when the target direction is known and stationary. However, spatially dynamic scenarios are considerably more challenging due to time-varying spatial features and arising ambiguities, e.g. when moving speakers cross. While in a static scenario it may be easy for a user to point to the target's direction, manually tracking a moving speaker is impractical. Instead of relying on accurate time-dependent directional cues, which we refer to as strong guidance, in this paper we propose a weakly guided extraction method solely depending on the target's initial position to cope with spatial dynamic scenarios. By incorporating our own deep tracking algorithm and developing a joint training strategy on a synthetic dataset, we demonstrate the proficiency of our approach in resolving spatial ambiguities and even outperform a mismatched, but strongly guided extraction method.", "date": "2025-05-20", "categories": []}
{"id": "2505.12079", "title": "SepPrune: Structured Pruning for Efficient Deep Speech Separation", "abstract": "Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational efficiency, an essential factor for low-latency speech processing in real-time applications. In this paper, we propose SepPrune, the first structured pruning framework specifically designed to compress deep speech separation models and reduce their computational cost. SepPrune begins by analyzing the computational structure of a given model to identify layers with the highest computational burden. It then introduces a differentiable masking strategy to enable gradient-driven channel selection. Based on the learned masks, SepPrune prunes redundant channels and fine-tunes the remaining parameters to recover performance. Extensive experiments demonstrate that this learnable pruning paradigm yields substantial advantages for channel pruning in speech separation models, outperforming existing methods. Notably, a model pruned with SepPrune can recover 85% of the performance of a pre-trained model (trained over hundreds of epochs) with only one epoch of fine-tuning, and achieves convergence 36$\\times$ faster than training from scratch. Code is available at https://github.com/itsnotacie/SepPrune.", "date": "2025-05-17", "categories": []}
{"id": "2505.12154", "title": "Learning to Highlight Audio by Watching Movies", "abstract": "Recent years have seen a significant increase in video content creation and consumption. Crafting engaging content requires the careful curation of both visual and audio elements. While visual cue curation, through techniques like optimal viewpoint selection or post-editing, has been central to media production, its natural counterpart, audio, has not undergone equivalent advancements. This often results in a disconnect between visual and acoustic saliency. To bridge this gap, we introduce a novel task: visually-guided acoustic highlighting, which aims to transform audio to deliver appropriate highlighting effects guided by the accompanying video, ultimately creating a more harmonious audio-visual experience. We propose a flexible, transformer-based multimodal framework to solve this task. To train our model, we also introduce a new dataset -- the muddy mix dataset, leveraging the meticulous audio and video crafting found in movies, which provides a form of free supervision. We develop a pseudo-data generation process to simulate poorly mixed audio, mimicking real-world scenarios through a three-step process -- separation, adjustment, and remixing. Our approach consistently outperforms several baselines in both quantitative and subjective evaluation. We also systematically study the impact of different types of contextual guidance and difficulty levels of the dataset. Our project page is here: https://wikichao.github.io/VisAH/.", "date": "2025-05-17", "categories": []}
{"id": "2505.12904", "title": "The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning", "abstract": "The increasing level of sound pollution in marine environments poses an increased threat to ocean health, making it crucial to monitor underwater noise. By monitoring this noise, the sources responsible for this pollution can be mapped. Monitoring is performed by passively listening to these sounds. This generates a large amount of data records, capturing a mix of sound sources such as ship activities and marine mammal vocalizations. Although machine learning offers a promising solution for automatic sound classification, current state-of-the-art methods implement supervised learning. This requires a large amount of high-quality labeled data that is not publicly available. In contrast, a massive amount of lower-quality unlabeled data is publicly available, offering the opportunity to explore unsupervised learning techniques. This research explores this possibility by implementing an unsupervised Contrastive Learning approach. Here, a Conformer-based encoder is optimized by the so-called Variance-Invariance-Covariance Regularization loss function on these lower-quality unlabeled data and the translation to the labeled data is made. Through classification tasks involving recognizing ship types and marine mammal vocalizations, our method demonstrates to produce robust and generalized embeddings. This shows to potential of unsupervised methods for various automatic underwater acoustic analysis tasks.", "date": "2025-05-19", "categories": []}
{"id": "2505.13094", "title": "Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation", "abstract": "Existing causal speech separation models often underperform compared to non-causal models due to difficulties in retaining historical information. To address this, we propose the Time-Frequency Attention Cache Memory (TFACM) model, which effectively captures spatio-temporal relationships through an attention mechanism and cache memory (CM) for historical information storage. In TFACM, an LSTM layer captures frequency-relative positions, while causal modeling is applied to the time dimension using local and global representations. The CM module stores past information, and the causal attention refinement (CAR) module further enhances time-based feature representations for finer granularity. Experimental results showed that TFACM achieveed comparable performance to the SOTA TF-GridNet-Causal model, with significantly lower complexity and fewer trainable parameters. For more details, visit the project page: https://cslikai.cn/TFACM/.", "date": "2025-05-19", "categories": []}
{"id": "2505.11375", "title": "Anti-aliasing of neural distortion effects via model fine tuning", "abstract": "Neural networks have become ubiquitous with guitar distortion effects modelling in recent years. Despite their ability to yield perceptually convincing models, they are susceptible to frequency aliasing when driven by high frequency and high gain inputs. Nonlinear activation functions create both the desired harmonic distortion and unwanted aliasing distortion as the bandwidth of the signal is expanded beyond the Nyquist frequency. Here, we present a method for reducing aliasing in neural models via a teacher-student fine tuning approach, where the teacher is a pre-trained model with its weights frozen, and the student is a copy of this with learnable parameters. The student is fine-tuned against an aliasing-free dataset generated by passing sinusoids through the original model and removing non-harmonic components from the output spectra. Our results show that this method significantly suppresses aliasing for both long-short-term-memory networks (LSTM) and temporal convolutional networks (TCN). In the majority of our case studies, the reduction in aliasing was greater than that achieved by two times oversampling. One side-effect of the proposed method is that harmonic distortion components are also affected. This adverse effect was found to be model-dependent, with the LSTM models giving the best balance between anti-aliasing and preserving the perceived similarity to an analog reference device.", "date": "2025-05-16", "categories": []}
{"id": "2505.09091", "title": "DPN-GAN: Inducing Periodic Activations in Generative Adversarial Networks for High-Fidelity Audio Synthesis", "abstract": "In recent years, generative adversarial networks (GANs) have made significant progress in generating audio sequences. However, these models typically rely on bandwidth-limited mel-spectrograms, which constrain the resolution of generated audio sequences, and lead to mode collapse during conditional generation. To address this issue, we propose Deformable Periodic Network based GAN (DPN-GAN), a novel GAN architecture that incorporates a kernel-based periodic ReLU activation function to induce periodic bias in audio generation. This innovative approach enhances the model's ability to capture and reproduce intricate audio patterns. In particular, our proposed model features a DPN module for multi-resolution generation utilizing deformable convolution operations, allowing for adaptive receptive fields that improve the quality and fidelity of the synthetic audio. Additionally, we enhance the discriminator network using deformable convolution to better distinguish between real and generated samples, further refining the audio quality. We trained two versions of the model: DPN-GAN small (38.67M parameters) and DPN-GAN large (124M parameters). For evaluation, we use five different datasets, covering both speech synthesis and music generation tasks, to demonstrate the efficiency of the DPN-GAN. The experimental results demonstrate that DPN-GAN delivers superior performance on both out-of-distribution and noisy data, showcasing its robustness and adaptability. Trained across various datasets, DPN-GAN outperforms state-of-the-art GAN architectures on standard evaluation metrics, and exhibits increased robustness in synthesized audio.", "date": "2025-05-14", "categories": []}
{"id": "2505.07709", "title": "ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration", "abstract": "This paper introduces ISAC, an invertible and stable, perceptually-motivated filter bank that is specifically designed to be integrated into machine learning paradigms. More precisely, the center frequencies and bandwidths of the filters are chosen to follow a non-linear, auditory frequency scale, the filter kernels have user-defined maximum temporal support and may serve as learnable convolutional kernels, and there exists a corresponding filter bank such that both form a perfect reconstruction pair. ISAC provides a powerful and user-friendly audio front-end suitable for any application, including analysis-synthesis schemes.", "date": "2025-05-12", "categories": []}
{"id": "2505.04451", "title": "Automatic Music Transcription using Convolutional Neural Networks and\n  Constant-Q transform", "abstract": "  Automatic music transcription (AMT) is the problem of analyzing an audio\nrecording of a musical piece and detecting notes that are being played. AMT is\na challenging problem, particularly when it comes to polyphonic music. The goal\nof AMT is to produce a score representation of a music piece, by analyzing a\nsound signal containing multiple notes played simultaneously. In this work, we\ndesign a processing pipeline that can transform classical piano audio files in\n.wav format into a music score representation. The features from the audio\nsignals are extracted using the constant-Q transform, and the resulting\ncoefficients are used as an input to the convolutional neural network (CNN)\nmodel.\n", "date": "2025-05-07", "categories": []}
{"id": "2505.01338", "title": "How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement\n  in Distant Microphone Scenarios", "abstract": "  Dereverberation is an important sub-task of Speech Enhancement (SE) to\nimprove the signal's intelligibility and quality. However, it remains\nchallenging because the reverberation is highly correlated with the signal.\nFurthermore, the single-channel SE literature has predominantly focused on\nrooms with short reverb times (typically under 1 second), smaller rooms (under\nvolumes of 1000 cubic meters) and relatively short distances (up to 2 meters).\nIn this paper, we explore real-time low-latency single-channel SE under distant\nmicrophone scenarios, such as 5 to 10 meters, and focus on conference rooms and\ntheatres, with larger room dimensions and reverberation times. Such a setup is\nuseful for applications such as lecture demonstrations, drama, and to enhance\nstage acoustics. First, we show that single-channel SE in such challenging\nscenarios is feasible. Second, we investigate the relationship between room\nvolume and reverberation time, and demonstrate its importance when randomly\nsimulating room impulse responses. Lastly, we show that for dereverberation\nwith short decay times, preserving early reflections before decaying the\ntransfer function of the room improves overall signal quality.\n", "date": "2025-05-02", "categories": []}
{"id": "2504.21366", "title": "DGFNet: End-to-End Audio-Visual Source Separation Based on Dynamic\n  Gating Fusion", "abstract": "  Current Audio-Visual Source Separation methods primarily adopt two design\nstrategies. The first strategy involves fusing audio and visual features at the\nbottleneck layer of the encoder, followed by processing the fused features\nthrough the decoder. However, when there is a significant disparity between the\ntwo modalities, this approach may lead to the loss of critical information. The\nsecond strategy avoids direct fusion and instead relies on the decoder to\nhandle the interaction between audio and visual features. Nonetheless, if the\nencoder fails to integrate information across modalities adequately, the\ndecoder may be unable to effectively capture the complex relationships between\nthem. To address these issues, this paper proposes a dynamic fusion method\nbased on a gating mechanism that dynamically adjusts the modality fusion\ndegree. This approach mitigates the limitations of solely relying on the\ndecoder and facilitates efficient collaboration between audio and visual\nfeatures. Additionally, an audio attention module is introduced to enhance the\nexpressive capacity of audio features, thereby further improving model\nperformance. Experimental results demonstrate that our method achieves\nsignificant performance improvements on two benchmark datasets, validating its\neffectiveness and advantages in Audio-Visual Source Separation tasks.\n", "date": "2025-04-30", "categories": []}
{"id": "2504.20625", "title": "DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models", "abstract": "  Room Impulse Responses (RIRs) characterize acoustic environments and are\ncrucial in multiple audio signal processing tasks. High-quality RIR estimates\ndrive applications such as virtual microphones, sound source localization,\naugmented reality, and data augmentation. However, obtaining RIR measurements\nwith high spatial resolution is resource-intensive, making it impractical for\nlarge spaces or when dense sampling is required. This research addresses the\nchallenge of estimating RIRs at unmeasured locations within a room using\nDenoising Diffusion Probabilistic Models (DDPM). Our method leverages the\nanalogy between RIR matrices and image inpainting, transforming RIR data into a\nformat suitable for diffusion-based reconstruction.\n  Using simulated RIR data based on the image method, we demonstrate our\napproach's effectiveness on microphone arrays of different curvatures, from\nlinear to semi-circular. Our method successfully reconstructs missing RIRs,\neven in large gaps between microphones. Under these conditions, it achieves\naccurate reconstruction, significantly outperforming baseline Spline Cubic\nInterpolation in terms of Normalized Mean Square Error and Cosine Distance\nbetween actual and interpolated RIRs.\n  This research highlights the potential of using generative models for\neffective RIR interpolation, paving the way for generating additional data from\nlimited real-world measurements.\n", "date": "2025-04-29", "categories": []}
{"id": "2410.06016", "title": "Variable Bitrate Residual Vector Quantization for Audio Coding", "abstract": "  Recent state-of-the-art neural audio compression models have progressively\nadopted residual vector quantization (RVQ). Despite this success, these models\nemploy a fixed number of codebooks per frame, which can be suboptimal in terms\nof rate-distortion tradeoff, particularly in scenarios with simple input audio,\nsuch as silence. To address this limitation, we propose variable bitrate RVQ\n(VRVQ) for audio codecs, which allows for more efficient coding by adapting the\nnumber of codebooks used per frame. Furthermore, we propose a gradient\nestimation method for the non-differentiable masking operation that transforms\nfrom the importance map to the binary importance mask, improving model training\nvia a straight-through estimator. We demonstrate that the proposed training\nframework achieves superior results compared to the baseline method and shows\nfurther improvement when applied to the current state-of-the-art codec.\n", "date": "2024-10-08", "categories": []}
{"id": "2504.17782", "title": "Unleashing the Power of Natural Audio Featuring Multiple Sound Sources", "abstract": "  Universal sound separation aims to extract clean audio tracks corresponding\nto distinct events from mixed audio, which is critical for artificial auditory\nperception. However, current methods heavily rely on artificially mixed audio\nfor training, which limits their ability to generalize to naturally mixed audio\ncollected in real-world environments. To overcome this limitation, we propose\nClearSep, an innovative framework that employs a data engine to decompose\ncomplex naturally mixed audio into multiple independent tracks, thereby\nallowing effective sound separation in real-world scenarios. We introduce two\nremix-based evaluation metrics to quantitatively assess separation quality and\nuse these metrics as thresholds to iteratively apply the data engine alongside\nmodel training, progressively optimizing separation performance. In addition,\nwe propose a series of training strategies tailored to these separated\nindependent tracks to make the best use of them. Extensive experiments\ndemonstrate that ClearSep achieves state-of-the-art performance across multiple\nsound separation tasks, highlighting its potential for advancing sound\nseparation in natural audio scenarios. For more examples and detailed results,\nplease visit our demo page at https://clearsep.github.io.\n", "date": "2025-04-24", "categories": []}
{"id": "2504.08371", "title": "Passive Underwater Acoustic Signal Separation based on Feature\n  Decoupling Dual-path Network", "abstract": "  Signal separation in the passive underwater acoustic domain has heavily\nrelied on deep learning techniques to isolate ship radiated noise. However, the\nseparation networks commonly used in this domain stem from speech separation\napplications and may not fully consider the unique aspects of underwater\nacoustics beforehand, such as the influence of different propagation media,\nsignal frequencies and modulation characteristics. This oversight highlights\nthe need for tailored approaches that account for the specific characteristics\nof underwater sound propagation. This study introduces a novel temporal network\ndesigned to separate ship radiated noise by employing a dual-path model and a\nfeature decoupling approach. The mixed signals' features are transformed into a\nspace where they exhibit greater independence, with each dimension's\nsignificance decoupled. Subsequently, a fusion of local and global attention\nmechanisms is employed in the separation layer. Extensive comparisons showcase\nthe effectiveness of this method when compared to other prevalent network\nmodels, as evidenced by its performance in the ShipsEar and DeepShip datasets.\n", "date": "2025-04-11", "categories": []}
{"id": "2504.07345", "title": "Quantum-Inspired Genetic Algorithm for Robust Source Separation in Smart\n  City Acoustics", "abstract": "  The cacophony of urban sounds presents a significant challenge for smart city\napplications that rely on accurate acoustic scene analysis. Effectively\nanalyzing these complex soundscapes, often characterized by overlapping sound\nsources, diverse acoustic events, and unpredictable noise levels, requires\nprecise source separation. This task becomes more complicated when only limited\ntraining data is available. This paper introduces a novel Quantum-Inspired\nGenetic Algorithm (p-QIGA) for source separation, drawing inspiration from\nquantum information theory to enhance acoustic scene analysis in smart cities.\nBy leveraging quantum superposition for efficient solution space exploration\nand entanglement to handle correlated sources, p-QIGA achieves robust\nseparation even with limited data. These quantum-inspired concepts are\nintegrated into a genetic algorithm framework to optimize source separation\nparameters. The effectiveness of our approach is demonstrated on two datasets:\nthe TAU Urban Acoustic Scenes 2020 Mobile dataset, representing typical urban\nsoundscapes, and the Silent Cities dataset, capturing quieter urban\nenvironments during the COVID-19 pandemic. Experimental results show that the\np-QIGA achieves accuracy comparable to state-of-the-art methods while\nexhibiting superior resilience to noise and limited training data, achieving up\nto 8.2 dB signal-to-distortion ratio (SDR) in noisy environments and\noutperforming baseline methods by up to 2 dB with only 10% of the training\ndata. This research highlights the potential of p-QIGA to advance acoustic\nsignal processing in smart cities, particularly for noise pollution monitoring\nand acoustic surveillance.\n", "date": "2025-04-10", "categories": []}
{"id": "2504.05576", "title": "SoundVista: Novel-View Ambient Sound Synthesis via Visual-Acoustic\n  Binding", "abstract": "  We introduce SoundVista, a method to generate the ambient sound of an\narbitrary scene at novel viewpoints. Given a pre-acquired recording of the\nscene from sparsely distributed microphones, SoundVista can synthesize the\nsound of that scene from an unseen target viewpoint. The method learns the\nunderlying acoustic transfer function that relates the signals acquired at the\ndistributed microphones to the signal at the target viewpoint, using a limited\nnumber of known recordings. Unlike existing works, our method does not require\nconstraints or prior knowledge of sound source details. Moreover, our method\nefficiently adapts to diverse room layouts, reference microphone configurations\nand unseen environments. To enable this, we introduce a visual-acoustic binding\nmodule that learns visual embeddings linked with local acoustic properties from\npanoramic RGB and depth data. We first leverage these embeddings to optimize\nthe placement of reference microphones in any given scene. During synthesis, we\nleverage multiple embeddings extracted from reference locations to get adaptive\nweights for their contribution, conditioned on target viewpoint. We benchmark\nthe task on both publicly available data and real-world settings. We\ndemonstrate significant improvements over existing methods.\n", "date": "2025-04-08", "categories": []}
{"id": "2504.02302", "title": "Causal Self-supervised Pretrained Frontend with Predictive Code for\n  Speech Separation", "abstract": "  Speech separation (SS) seeks to disentangle a multi-talker speech mixture\ninto single-talker speech streams. Although SS can be generally achieved using\noffline methods, such a processing paradigm is not suitable for real-time\nstreaming applications. Causal separation models, which rely only on past and\npresent information, offer a promising solution for real-time streaming.\nHowever, these models typically suffer from notable performance degradation due\nto the absence of future context. In this paper, we introduce a novel frontend\nthat is designed to mitigate the mismatch between training and run-time\ninference by implicitly incorporating future information into causal models\nthrough predictive patterns. The pretrained frontend employs a transformer\ndecoder network with a causal convolutional encoder as the backbone and is\npretrained in a self-supervised manner with two innovative pretext tasks:\nautoregressive hybrid prediction and contextual knowledge distillation. These\ntasks enable the model to capture predictive patterns directly from mixtures in\na self-supervised manner. The pretrained frontend subsequently serves as a\nfeature extractor to generate high-quality predictive patterns. Comprehensive\nevaluations on synthetic and real-world datasets validated the effectiveness of\nthe proposed pretrained frontend.\n", "date": "2025-04-03", "categories": []}
{"id": "2504.00750", "title": "$C^2$AV-TSE: Context and Confidence-aware Audio Visual Target Speaker\n  Extraction", "abstract": "  Audio-Visual Target Speaker Extraction (AV-TSE) aims to mimic the human\nability to enhance auditory perception using visual cues. Although numerous\nmodels have been proposed recently, most of them estimate target signals by\nprimarily relying on local dependencies within acoustic features,\nunderutilizing the human-like capacity to infer unclear parts of speech through\ncontextual information. This limitation results in not only suboptimal\nperformance but also inconsistent extraction quality across the utterance, with\nsome segments exhibiting poor quality or inadequate suppression of interfering\nspeakers. To close this gap, we propose a model-agnostic strategy called the\nMask-And-Recover (MAR). It integrates both inter- and intra-modality contextual\ncorrelations to enable global inference within extraction modules.\nAdditionally, to better target challenging parts within each sample, we\nintroduce a Fine-grained Confidence Score (FCS) model to assess extraction\nquality and guide extraction modules to emphasize improvement on low-quality\nsegments. To validate the effectiveness of our proposed model-agnostic training\nparadigm, six popular AV-TSE backbones were adopted for evaluation on the\nVoxCeleb2 dataset, demonstrating consistent performance improvements across\nvarious metrics.\n", "date": "2025-04-01", "categories": []}
{"id": "2412.08247", "title": "MoMuSE: Momentum Multi-modal Target Speaker Extraction for Real-time\n  Scenarios with Impaired Visual Cues", "abstract": "  Audio-visual Target Speaker Extraction (AV-TSE) aims to isolate the speech of\na specific target speaker from an audio mixture using time-synchronized visual\ncues. In real-world scenarios, visual cues are not always available due to\nvarious impairments, which undermines the stability of AV-TSE. Despite this\nchallenge, humans can maintain attentional momentum over time, even when the\ntarget speaker is not visible. In this paper, we introduce the Momentum\nMulti-modal target Speaker Extraction (MoMuSE), which retains a speaker\nidentity momentum in memory, enabling the model to continuously track the\ntarget speaker. Designed for real-time inference, MoMuSE extracts the current\nspeech window with guidance from both visual cues and dynamically updated\nspeaker momentum. Experimental results demonstrate that MoMuSE exhibits\nsignificant improvement, particularly in scenarios with severe impairment of\nvisual cues.\n", "date": "2024-12-11", "categories": []}
{"id": "2408.07472", "title": "Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation\n  with Diffusion Models", "abstract": "  This paper presents an unsupervised method for single-channel blind\ndereverberation and room impulse response (RIR) estimation, called BUDDy. The\nalgorithm is rooted in Bayesian posterior sampling: it combines a likelihood\nmodel enforcing fidelity to the reverberant measurement, and an anechoic speech\nprior implemented by an unconditional diffusion model. We design a parametric\nfilter representing the RIR, with exponential decay for each frequency subband.\nRoom acoustics estimation and speech dereverberation are jointly carried out,\nas the filter parameters are iteratively estimated and the speech utterance\nrefined along the reverse diffusion trajectory. In a blind scenario where the\nRIR is unknown, BUDDy successfully performs speech dereverberation in various\nacoustic scenarios, significantly outperforming other blind unsupervised\nbaselines. Unlike supervised methods, which often struggle to generalize, BUDDy\nseamlessly adapts to different acoustic conditions. This paper extends our\nprevious work by offering new experimental results and insights into the\nalgorithm's versatility. We demonstrate the robustness of our proposed method\nto new acoustic and speaker conditions, as well as its adaptability to\nhigh-resolution singing voice dereverberation, using both instrumental metrics\nand subjective listening evaluation. We study BUDDy's performance for RIR\nestimation and observe it surpasses a state-of-the-art supervised DNN-based\nestimator on mismatched acoustic conditions. Finally, we investigate the\nsensitivity of informed dereverberation methods to RIR estimation errors,\nthereby motivating the joint acoustic estimation and dereverberation design.\nAudio examples and code can be found online.\n", "date": "2024-08-14", "categories": []}
{"id": "2409.13881", "title": "Investigation of Time-Frequency Feature Combinations with Histogram\n  Layer Time Delay Neural Networks", "abstract": "  While deep learning has reduced the prevalence of manual feature extraction,\ntransformation of data via feature engineering remains essential for improving\nmodel performance, particularly for underwater acoustic signals. The methods by\nwhich audio signals are converted into time-frequency representations and the\nsubsequent handling of these spectrograms can significantly impact performance.\nThis work demonstrates the performance impact of using different combinations\nof time-frequency features in a histogram layer time delay neural network. An\noptimal set of features is identified with results indicating that specific\nfeature combinations outperform single data features.\n", "date": "2024-09-20", "categories": []}
{"id": "2503.08798", "title": "Contextual Speech Extraction: Leveraging Textual History as an Implicit\n  Cue for Target Speech Extraction", "abstract": "  In this paper, we investigate a novel approach for Target Speech Extraction\n(TSE), which relies solely on textual context to extract the target speech. We\nrefer to this task as Contextual Speech Extraction (CSE). Unlike traditional\nTSE methods that rely on pre-recorded enrollment utterances, video of the\ntarget speaker's face, spatial information, or other explicit cues to identify\nthe target stream, our proposed method requires only a few turns of previous\ndialogue (or monologue) history. This approach is naturally feasible in mobile\nmessaging environments where voice recordings are typically preceded by textual\ndialogue that can be leveraged implicitly. We present three CSE models and\nanalyze their performances on three datasets. Through our experiments, we\ndemonstrate that even when the model relies purely on dialogue history, it can\nachieve over 90 % accuracy in identifying the correct target stream with only\ntwo previous dialogue turns. Furthermore, we show that by leveraging both\ntextual context and enrollment utterances as cues during training, we further\nenhance our model's flexibility and effectiveness, allowing us to use either\ncue during inference, or combine both for improved performance. Samples and\ncode available on https://miraodasilva.github.io/cse-project-page .\n", "date": "2025-03-11", "categories": []}
{"id": "2501.04926", "title": "FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with\n  Single-Step Flow Matching", "abstract": "  Audio super-resolution is challenging owing to its ill-posed nature.\nRecently, the application of diffusion models in audio super-resolution has\nshown promising results in alleviating this challenge. However, diffusion-based\nmodels have limitations, primarily the necessity for numerous sampling steps,\nwhich causes significantly increased latency when synthesizing high-quality\naudio samples. In this paper, we propose FLowHigh, a novel approach that\nintegrates flow matching, a highly efficient generative model, into audio\nsuper-resolution. We also explore probability paths specially tailored for\naudio super-resolution, which effectively capture high-resolution audio\ndistributions, thereby enhancing reconstruction quality. The proposed method\ngenerates high-fidelity, high-resolution audio through a single-step sampling\nprocess across various input sampling rates. The experimental results on the\nVCTK benchmark dataset demonstrate that FLowHigh achieves state-of-the-art\nperformance in audio super-resolution, as evaluated by log-spectral distance\nand ViSQOL while maintaining computational efficiency with only a single-step\nsampling process.\n", "date": "2025-01-09", "categories": []}
{"id": "2409.18239", "title": "Towards Sub-millisecond Latency Real-Time Speech Enhancement Models on\n  Hearables", "abstract": "  Low latency models are critical for real-time speech enhancement\napplications, such as hearing aids and hearables. However, the sub-millisecond\nlatency space for resource-constrained hearables remains underexplored. We\ndemonstrate speech enhancement using a computationally efficient minimum-phase\nFIR filter, enabling sample-by-sample processing to achieve mean algorithmic\nlatency of 0.32 ms to 1.25 ms. With a single microphone, we observe a mean\nSI-SDRi of 4.1 dB. The approach shows generalization with a DNSMOS increase of\n0.2 on unseen audio recordings. We use a lightweight LSTM-based model of 626k\nparameters to generate FIR taps. Using a real hardware implementation on a\nlow-power DSP, our system can run with 376 MIPS and a mean end-to-end latency\nof 3.35 ms. In addition, we provide a comparison with existing low-latency\nspectral masking techniques. We hope this work will enable a better\nunderstanding of latency and can be used to improve the comfort and usability\nof hearables.\n", "date": "2024-09-26", "categories": []}
{"id": "2503.01485", "title": "FlowDec: A flow-based full-band general audio codec with high perceptual\n  quality", "abstract": "  We propose FlowDec, a neural full-band audio codec for general audio sampled\nat 48 kHz that combines non-adversarial codec training with a stochastic\npostfilter based on a novel conditional flow matching method. Compared to the\nprior work ScoreDec which is based on score matching, we generalize from speech\nto general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving\noutput quality and reducing the required postfilter DNN evaluations from 60 to\n6 without any fine-tuning or distillation techniques. We provide theoretical\ninsights and geometric intuitions for our approach in comparison to ScoreDec as\nwell as another recent work that uses flow matching, and conduct ablation\nstudies on our proposed components. We show that FlowDec is a competitive\nalternative to the recent GAN-dominated stream of neural codecs, achieving FAD\nscores better than those of the established GAN-based codec DAC and listening\ntest scores that are on par, and producing qualitatively more natural\nreconstructions for speech and harmonic structures in music.\n", "date": "2025-03-03", "categories": []}
{"id": "2309.08454", "title": "Combining TF-GridNet and Mixture Encoder for Continuous Speech\n  Separation for Meeting Transcription", "abstract": "  Many real-life applications of automatic speech recognition (ASR) require\nprocessing of overlapped speech. A common method involves first separating the\nspeech into overlap-free streams on which ASR is performed. Recently,\nTF-GridNet has shown impressive performance in speech separation in real\nreverberant conditions. Furthermore, a mixture encoder was proposed that\nleverages the mixed speech to mitigate the effect of separation artifacts. In\nthis work, we extended the mixture encoder from a static two-speaker scenario\nto a natural meeting context featuring an arbitrary number of speakers and\nvarying degrees of overlap. We further demonstrate its limits by the\nintegration with separators of varying strength including TF-GridNet. Our\nexperiments result in a new state-of-the-art performance on LibriCSS using a\nsingle microphone. They show that TF-GridNet largely closes the gap between\nprevious methods and oracle separation independent of mixture encoding. We\nfurther investigate the remaining potential for improvement.\n", "date": "2023-09-15", "categories": []}
{"id": "2502.17527", "title": "Perceptual Noise-Masking with Music through Deep Spectral Envelope\n  Shaping", "abstract": "  People often listen to music in noisy environments, seeking to isolate\nthemselves from ambient sounds. Indeed, a music signal can mask some of the\nnoise's frequency components due to the effect of simultaneous masking. In this\narticle, we propose a neural network based on a psychoacoustic masking model,\ndesigned to enhance the music's ability to mask ambient noise by reshaping its\nspectral envelope with predicted filter frequency responses. The model is\ntrained with a perceptual loss function that balances two constraints:\neffectively masking the noise while preserving the original music mix and the\nuser's chosen listening level. We evaluate our approach on simulated data\nreplicating a user's experience of listening to music with headphones in a\nnoisy environment. The results, based on defined objective metrics, demonstrate\nthat our system improves the state of the art.\n", "date": "2025-02-24", "categories": []}
{"id": "2409.10995", "title": "SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source\n  Separation", "abstract": "  Recent advancements in music source separation have significantly progressed,\nparticularly in isolating vocals, drums, and bass elements from mixed tracks.\nThese developments owe much to the creation and use of large-scale, multitrack\ndatasets dedicated to these specific components. However, the challenge of\nextracting similarly sounding sources from orchestra recordings has not been\nextensively explored, largely due to a scarcity of comprehensive and clean (i.e\nbleed-free) multitrack datasets. In this paper, we introduce a novel multitrack\ndataset called SynthSOD, developed using a set of simulation techniques to\ncreate a realistic (i.e. using high-quality soundfonts), musically motivated,\nand heterogeneous training set comprising different dynamics, natural tempo\nchanges, styles, and conditions. Moreover, we demonstrate the application of a\nwidely used baseline music separation model trained on our synthesized dataset\nw.r.t to the well-known EnsembleSet, and evaluate its performance under both\nsynthetic and real-world conditions.\n", "date": "2024-09-17", "categories": []}
{"id": "2410.14945", "title": "ImmerseDiffusion: A Generative Spatial Audio Latent Diffusion Model", "abstract": "  We introduce ImmerseDiffusion, an end-to-end generative audio model that\nproduces 3D immersive soundscapes conditioned on the spatial, temporal, and\nenvironmental conditions of sound objects. ImmerseDiffusion is trained to\ngenerate first-order ambisonics (FOA) audio, which is a conventional spatial\naudio format comprising four channels that can be rendered to multichannel\nspatial output. The proposed generative system is composed of a spatial audio\ncodec that maps FOA audio to latent components, a latent diffusion model\ntrained based on various user input types, namely, text prompts, spatial,\ntemporal and environmental acoustic parameters, and optionally a spatial audio\nand text encoder trained in a Contrastive Language and Audio Pretraining (CLAP)\nstyle. We propose metrics to evaluate the quality and spatial adherence of the\ngenerated spatial audio. Finally, we assess the model performance in terms of\ngeneration quality and spatial conformance, comparing the two proposed modes:\n``descriptive\", which uses spatial text prompts) and ``parametric\", which uses\nnon-spatial text prompts and spatial parameters. Our evaluations demonstrate\npromising results that are consistent with the user conditions and reflect\nreliable spatial fidelity.\n", "date": "2024-10-19", "categories": []}
{"id": "2502.06285", "title": "End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer\n  Functions", "abstract": "  This paper introduces a multi-microphone method for extracting a desired\nspeaker from a mixture involving multiple speakers and directional noise in a\nreverberant environment. In this work, we propose leveraging the instantaneous\nrelative transfer function (RTF), estimated from a reference utterance recorded\nin the same position as the desired source. The effectiveness of the RTF-based\nspatial cue is compared with direction of arrival (DOA)-based spatial cue and\nthe conventional spectral embedding. Experimental results in challenging\nacoustic scenarios demonstrate that using spatial cues yields better\nperformance than the spectral-based cue and that the instantaneous RTF\noutperforms the DOA-based spatial cue.\n", "date": "2025-02-10", "categories": []}
{"id": "2502.06364", "title": "Automatic Identification of Samples in Hip-Hop Music via Multi-Loss\n  Training and an Artificial Dataset", "abstract": "  Sampling, the practice of reusing recorded music or sounds from another\nsource in a new work, is common in popular music genres like hip-hop and rap.\nNumerous services have emerged that allow users to identify connections between\nsamples and the songs that incorporate them, with the goal of enhancing music\ndiscovery. Designing a system that can perform the same task automatically is\nchallenging, as samples are commonly altered with audio effects like pitch- and\ntime-stretching and may only be seconds long. Progress on this task has been\nminimal and is further blocked by the limited availability of training data.\nHere, we show that a convolutional neural network trained on an artificial\ndataset can identify real-world samples in commercial hip-hop music. We extract\nvocal, harmonic, and percussive elements from several databases of\nnon-commercial music recordings using audio source separation, and train the\nmodel to fingerprint a subset of these elements in transformed versions of the\noriginal audio. We optimize the model using a joint classification and metric\nlearning loss and show that it achieves 13% greater precision on real-world\ninstances of sampling than a fingerprinting system using acoustic landmarks,\nand that it can recognize samples that have been both pitch shifted and time\nstretched. We also show that, for half of the commercial music recordings we\ntested, our model is capable of locating the position of a sample to within\nfive seconds.\n", "date": "2025-02-10", "categories": []}
{"id": "2405.11831", "title": "SSAMBA: Self-Supervised Audio Representation Learning with Mamba State\n  Space Model", "abstract": "  Transformers have revolutionized deep learning across various tasks,\nincluding audio representation learning, due to their powerful modeling\ncapabilities. However, they often suffer from quadratic complexity in both GPU\nmemory usage and computational inference time, affecting their efficiency.\nRecently, state space models (SSMs) like Mamba have emerged as a promising\nalternative, offering a more efficient approach by avoiding these complexities.\nGiven these advantages, we explore the potential of SSM-based models in audio\ntasks. In this paper, we introduce Self-Supervised Audio Mamba (SSAMBA), the\nfirst self-supervised, attention-free, and SSM-based model for audio\nrepresentation learning. SSAMBA leverages the bidirectional Mamba to capture\ncomplex audio patterns effectively. We incorporate a self-supervised\npretraining framework that optimizes both discriminative and generative\nobjectives, enabling the model to learn robust audio representations from\nlarge-scale, unlabeled datasets. We evaluated SSAMBA on various tasks such as\naudio classification, keyword spotting, and speaker identification. Our results\ndemonstrate that SSAMBA outperforms the Self-Supervised Audio Spectrogram\nTransformer (SSAST) in most tasks. Notably, SSAMBA is approximately 92.7%\nfaster in batch inference speed and 95.4% more memory-efficient than SSAST for\nthe tiny model size with an input token size of 22k. These efficiency gains,\ncombined with superior performance, underscore the effectiveness of SSAMBA's\narchitectural innovation, making it a compelling choice for a wide range of\naudio processing applications.\n", "date": "2024-05-20", "categories": []}
{"id": "2310.13404", "title": "Definition-independent Formalization of Soundscapes: Towards a Formal\n  Methodology", "abstract": "  Soundscapes have been studied by researchers from various disciplines, each\nwith different perspectives, goals, approaches, and terminologies. Accordingly,\ndepending on the field, the concept of a soundscape's components changes,\nconsequently changing the basic definition. This results in complicating\ninterdisciplinary communication and comparison of results. Especially when\nsoundscape-unrelated research areas are involved. For this reason, we present a\npotential formalization that is independent of the underlying soundscape\ndefinition, with the goal of being able to capture the heterogeneous structure\nof the data as well as the different ideologies in one model. In an exemplary\nanalysis of frequency correlation matrices for land use type detection as an\nalternative to features like MFCCs, we show a practical application of our\npresented formalization.\n", "date": "2023-10-20", "categories": []}
{"id": "2501.16171", "title": "Separate This, and All of these Things Around It: Music Source\n  Separation via Hyperellipsoidal Queries", "abstract": "  Music source separation is an audio-to-audio retrieval task of extracting one\nor more constituent components, or composites thereof, from a musical audio\nmixture. Each of these constituent components is often referred to as a \"stem\"\nin literature. Historically, music source separation has been dominated by a\nstem-based paradigm, leading to most state-of-the-art systems being either a\ncollection of single-stem extraction models, or a tightly coupled system with a\nfixed, difficult-to-modify, set of supported stems. Combined with the limited\ndata availability, advances in music source separation have thus been mostly\nlimited to the \"VDBO\" set of stems: \\textit{vocals}, \\textit{drum},\n\\textit{bass}, and the catch-all \\textit{others}. Recent work in music source\nseparation has begun to challenge the fixed-stem paradigm, moving towards\nmodels able to extract any musical sound as long as this target type of sound\ncould be specified to the model as an additional query input. We generalize\nthis idea to a \\textit{query-by-region} source separation system, specifying\nthe target based on the query regardless of how many sound sources or which\nsound classes are contained within it. To do so, we propose the use of\nhyperellipsoidal regions as queries to allow for an intuitive yet easily\nparametrizable approach to specifying both the target (location) as well as its\nspread. Evaluation of the proposed system on the MoisesDB dataset demonstrated\nstate-of-the-art performance of the proposed system both in terms of\nsignal-to-noise ratios and retrieval metrics.\n", "date": "2025-01-27", "categories": []}
{"id": "2501.14610", "title": "Leveraging Spatial Cues from Cochlear Implant Microphones to Efficiently\n  Enhance Speech Separation in Real-World Listening Scenes", "abstract": "  Speech separation approaches for single-channel, dry speech mixtures have\nsignificantly improved. However, real-world spatial and reverberant acoustic\nenvironments remain challenging, limiting the effectiveness of these approaches\nfor assistive hearing devices like cochlear implants (CIs). To address this, we\nquantify the impact of real-world acoustic scenes on speech separation and\nexplore how spatial cues can enhance separation quality efficiently. We analyze\nperformance based on implicit spatial cues (inherent in the acoustic input and\nlearned by the model) and explicit spatial cues (manually calculated spatial\nfeatures added as auxiliary inputs). Our findings show that spatial cues (both\nimplicit and explicit) improve separation for mixtures with spatially separated\nand nearby talkers. Furthermore, spatial cues enhance separation when spectral\ncues are ambiguous, such as when voices are similar. Explicit spatial cues are\nparticularly beneficial when implicit spatial cues are weak. For instance,\nsingle CI microphone recordings provide weaker implicit spatial cues than\nbilateral CIs, but even single CIs benefit from explicit cues. These results\nemphasize the importance of training models on real-world data to improve\ngeneralizability in everyday listening scenarios. Additionally, our statistical\nanalyses offer insights into how data properties influence model performance,\nsupporting the development of efficient speech separation approaches for CIs\nand other assistive devices in real-world settings.\n", "date": "2025-01-24", "categories": []}
{"id": "2501.10755", "title": "An Experimental Study on Joint Modeling for Sound Event Localization and\n  Detection with Source Distance Estimation", "abstract": "  In traditional sound event localization and detection (SELD) tasks, the focus\nis typically on sound event detection (SED) and direction-of-arrival (DOA)\nestimation, but they fall short of providing full spatial information about the\nsound source. The 3D SELD task addresses this limitation by integrating source\ndistance estimation (SDE), allowing for complete spatial localization. We\npropose three approaches to tackle this challenge: a novel method with\nindependent training and joint prediction, which firstly treats DOA and\ndistance estimation as separate tasks and then combines them to solve 3D SELD;\na dual-branch representation with source Cartesian coordinate used for\nsimultaneous DOA and distance estimation; and a three-branch structure that\njointly models SED, DOA, and SDE within a unified framework. Our proposed\nmethod ranked first in the DCASE 2024 Challenge Task 3, demonstrating the\neffectiveness of joint modeling for addressing the 3D SELD task. The relevant\ncode for this paper will be open-sourced in the future.\n", "date": "2025-01-18", "categories": []}
{"id": "2408.12549", "title": "Modeling Time-Variant Responses of Optical Compressors with Selective\n  State Space Models", "abstract": "  This paper presents a method for modeling optical dynamic range compressors\nusing deep neural networks with Selective State Space models. The proposed\napproach surpasses previous methods based on recurrent layers by employing a\nSelective State Space block to encode the input audio. It features a refined\ntechnique integrating Feature-wise Linear Modulation and Gated Linear Units to\nadjust the network dynamically, conditioning the compression's attack and\nrelease phases according to external parameters. The proposed architecture is\nwell-suited for low-latency and real-time applications, crucial in live audio\nprocessing. The method has been validated on the analog optical compressors\nTubeTech CL 1B and Teletronix LA-2A, which possess distinct characteristics.\nEvaluation is performed using quantitative metrics and subjective listening\ntests, comparing the proposed method with other state-of-the-art models.\nResults show that our black-box modeling methods outperform all others,\nachieving accurate emulation of the compression process for both seen and\nunseen settings during training. We further show a correlation between this\naccuracy and the sampling density of the control parameters in the dataset and\nidentify settings with fast attack and slow release as the most challenging to\nemulate.\n", "date": "2024-08-22", "categories": []}
{"id": "2501.08047", "title": "Gen-A: Generalizing Ambisonics Neural Encoding to Unseen Microphone\n  Arrays", "abstract": "  Using deep neural networks (DNNs) for encoding of microphone array (MA)\nsignals to the Ambisonics spatial audio format can surpass certain limitations\nof established conventional methods, but existing DNN-based methods need to be\ntrained separately for each MA. This paper proposes a DNN-based method for\nAmbisonics encoding that can generalize to arbitrary MA geometries unseen\nduring training. The method takes as inputs the MA geometry and MA signals and\nuses a multi-level encoder consisting of separate paths for geometry and signal\ndata, where geometry features inform the signal encoder at each level. The\nmethod is validated in simulated anechoic and reverberant conditions with one\nand two sources. The results indicate improvement over conventional encoding\nacross the whole frequency range for dry scenes, while for reverberant scenes\nthe improvement is frequency-dependent.\n", "date": "2025-01-14", "categories": []}
{"id": "2411.03172", "title": "Blind Estimation of Sub-band Acoustic Parameters from Ambisonics\n  Recordings using Spectro-Spatial Covariance Features", "abstract": "  Estimating frequency-varying acoustic parameters is essential for enhancing\nimmersive perception in realistic spatial audio creation. In this paper, we\npropose a unified framework that blindly estimates reverberation time (T60),\ndirect-to-reverberant ratio (DRR), and clarity (C50) across 10 frequency bands\nusing first-order Ambisonics (FOA) speech recordings as inputs. The proposed\nframework utilizes a novel feature named Spectro-Spatial Covariance Vector\n(SSCV), efficiently representing temporal, spectral as well as spatial\ninformation of the FOA signal. Our models significantly outperform existing\nsingle-channel methods with only spectral information, reducing estimation\nerrors by more than half for all three acoustic parameters. Additionally, we\nintroduce FOA-Conv3D, a novel back-end network for effectively utilising the\nSSCV feature with a 3D convolutional encoder. FOA-Conv3D outperforms the\nconvolutional neural network (CNN) and recurrent convolutional neural network\n(CRNN) backends, achieving lower estimation errors and accounting for a higher\nproportion of variance (PoV) for all 3 acoustic parameters.\n", "date": "2024-11-05", "categories": []}
{"id": "2410.19595", "title": "Mask-Weighted Spatial Likelihood Coding for Speaker-Independent Joint\n  Localization and Mask Estimation", "abstract": "  Due to their robustness and flexibility, neural-driven beamformers are a\npopular choice for speech separation in challenging environments with a varying\namount of simultaneous speakers alongside noise and reverberation.\nTime-frequency masks and relative directions of the speakers regarding a fixed\nspatial grid can be used to estimate the beamformer's parameters. To some\ndegree, speaker-independence is achieved by ensuring a greater amount of\nspatial partitions than speech sources. In this work, we analyze how to encode\nboth mask and positioning into such a grid to enable joint estimation of both\nquantities. We propose mask-weighted spatial likelihood coding and show that it\nachieves considerable performance in both tasks compared to baseline encodings\noptimized for either localization or mask estimation. In the same setup, we\ndemonstrate superiority for joint estimation of both quantities. Conclusively,\nwe propose a universal approach which can replace an upstream sound source\nlocalization system solely by adapting the training framework, making it highly\nrelevant in performance-critical scenarios.\n", "date": "2024-10-25", "categories": []}
{"id": "2409.12444", "title": "A Lightweight and Real-Time Binaural Speech Enhancement Model with\n  Spatial Cues Preservation", "abstract": "  Binaural speech enhancement (BSE) aims to jointly improve the speech quality\nand intelligibility of noisy signals received by hearing devices and preserve\nthe spatial cues of the target for natural listening. Existing methods often\nsuffer from the compromise between noise reduction (NR) capacity and spatial\ncues preservation (SCP) accuracy and a high computational demand in complex\nacoustic scenes. In this work, we present a learning-based lightweight binaural\ncomplex convolutional network (LBCCN), which excels in NR by filtering\nlow-frequency bands and keeping the rest. Additionally, our approach explicitly\nincorporates the estimation of interchannel relative acoustic transfer function\nto ensure the spatial cues fidelity and speech clarity. Results show that the\nproposed LBCCN can achieve a comparable NR performance to state-of-the-art\nmethods under fixed-speaker conditions, but with a much lower computational\ncost and a certain degree of SCP capability. The reproducible code and audio\nexamples are available at https://github.com/jywanng/LBCCN.\n", "date": "2024-09-19", "categories": []}
{"id": "2408.07234", "title": "Direction of Arrival Correction through Speech Quality Feedback", "abstract": "  Real-time speech enhancement has began to rise in performance, and the Demucs\nDenoiser model has recently demonstrated strong performance in\nmultiple-speech-source scenarios when accompanied by a location-based speech\ntarget selection strategy. However, it has shown to be sensitive to errors in\nthe direction-of-arrival (DOA) estimation. In this work, a DOA correction\nscheme is proposed that uses the real-time estimated speech quality of its\nenhanced output as the observed variable in an Adam-based optimization feedback\nloop to find the correct DOA. In spite of the high variability of the speech\nquality estimation, the proposed system is able to correct in real-time an\nerror of up to 15$^o$ using only the speech quality as its guide. Several\ninsights are provided for future versions of the proposed system to speed up\nconvergence and further reduce the speech quality estimation variability.\n", "date": "2024-08-13", "categories": []}
{"id": "2409.08514", "title": "Apollo: Band-sequence Modeling for High-Quality Audio Restoration", "abstract": "  Audio restoration has become increasingly significant in modern society, not\nonly due to the demand for high-quality auditory experiences enabled by\nadvanced playback devices, but also because the growing capabilities of\ngenerative audio models necessitate high-fidelity audio. Typically, audio\nrestoration is defined as a task of predicting undistorted audio from damaged\ninput, often trained using a GAN framework to balance perception and\ndistortion. Since audio degradation is primarily concentrated in mid- and\nhigh-frequency ranges, especially due to codecs, a key challenge lies in\ndesigning a generator capable of preserving low-frequency information while\naccurately reconstructing high-quality mid- and high-frequency content.\nInspired by recent advancements in high-sample-rate music separation, speech\nenhancement, and audio codec models, we propose Apollo, a generative model\ndesigned for high-sample-rate audio restoration. Apollo employs an explicit\nfrequency band split module to model the relationships between different\nfrequency bands, allowing for more coherent and higher-quality restored audio.\nEvaluated on the MUSDB18-HQ and MoisesDB datasets, Apollo consistently\noutperforms existing SR-GAN models across various bit rates and music genres,\nparticularly excelling in complex scenarios involving mixtures of multiple\ninstruments and vocals. Apollo significantly improves music restoration quality\nwhile maintaining computational efficiency. The source code for Apollo is\npublicly available at https://github.com/JusperLee/Apollo.\n", "date": "2024-09-13", "categories": []}
{"id": "2306.11509", "title": "Implicit neural representation with physics-informed neural networks for\n  the reconstruction of the early part of room impulse responses", "abstract": "  Recently deep learning and machine learning approaches have been widely\nemployed for various applications in acoustics. Nonetheless, in the area of\nsound field processing and reconstruction classic methods based on the\nsolutions of wave equation are still widespread. Recently, physics-informed\nneural networks have been proposed as a deep learning paradigm for solving\npartial differential equations which govern physical phenomena, bridging the\ngap between purely data-driven and model based methods. Here, we exploit\nphysics-informed neural networks to reconstruct the early part of missing room\nimpulse responses in an uniform linear array. This methodology allows us to\nexploit the underlying law of acoustics, i.e., the wave equation, forcing the\nneural network to generate physically meaningful solutions given only a limited\nnumber of data points. The results on real measurements show that the proposed\nmodel achieves accurate reconstruction and performance in line with respect to\nstate-of-the-art deep-learning and compress sensing techniques while\nmaintaining a lightweight architecture.\n", "date": "2023-06-20", "categories": []}
{"id": "2501.03045", "title": "Single-Channel Distance-Based Source Separation for Mobile GPU in\n  Outdoor and Indoor Environments", "abstract": "  This study emphasizes the significance of exploring distance-based source\nseparation (DSS) in outdoor environments. Unlike existing studies that\nprimarily focus on indoor settings, the proposed model is designed to capture\nthe unique characteristics of outdoor audio sources. It incorporates advanced\ntechniques, including a two-stage conformer block, a linear relation-aware\nself-attention (RSA), and a TensorFlow Lite GPU delegate. While the linear RSA\nmay not capture physical cues as explicitly as the quadratic RSA, the linear\nRSA enhances the model's context awareness, leading to improved performance on\nthe DSS that requires an understanding of physical cues in outdoor and indoor\nenvironments. The experimental results demonstrated that the proposed model\novercomes the limitations of existing approaches and considerably enhances\nenergy efficiency and real-time inference speed on mobile devices.\n", "date": "2025-01-06", "categories": []}
{"id": "2412.16861", "title": "SoundLoc3D: Invisible 3D Sound Source Localization and Classification\n  Using a Multimodal RGB-D Acoustic Camera", "abstract": "  Accurately localizing 3D sound sources and estimating their semantic labels\n-- where the sources may not be visible, but are assumed to lie on the physical\nsurface of objects in the scene -- have many real applications, including\ndetecting gas leak and machinery malfunction. The audio-visual weak-correlation\nin such setting poses new challenges in deriving innovative methods to answer\nif or how we can use cross-modal information to solve the task. Towards this\nend, we propose to use an acoustic-camera rig consisting of a pinhole RGB-D\ncamera and a coplanar four-channel microphone array~(Mic-Array). By using this\nrig to record audio-visual signals from multiviews, we can use the cross-modal\ncues to estimate the sound sources 3D locations. Specifically, our framework\nSoundLoc3D treats the task as a set prediction problem, each element in the set\ncorresponds to a potential sound source. Given the audio-visual\nweak-correlation, the set representation is initially learned from a single\nview microphone array signal, and then refined by actively incorporating\nphysical surface cues revealed from multiview RGB-D images. We demonstrate the\nefficiency and superiority of SoundLoc3D on large-scale simulated dataset, and\nfurther show its robustness to RGB-D measurement inaccuracy and ambient noise\ninterference.\n", "date": "2024-12-22", "categories": []}
{"id": "2412.19471", "title": "Meta-Learning-Based Delayless Subband Adaptive Filter using Complex\n  Self-Attention for Active Noise Control", "abstract": "  Active noise control typically employs adaptive filtering to generate\nsecondary noise, where the least mean square algorithm is the most widely used.\nHowever, traditional updating rules are linear and exhibit limited\neffectiveness in addressing nonlinear environments and nonstationary noise. To\ntackle this challenge, we reformulate the active noise control problem as a\nmeta-learning problem and propose a meta-learning-based delayless subband\nadaptive filter with deep neural networks. The core idea is to utilize a neural\nnetwork as an adaptive algorithm that can adapt to different environments and\ntypes of noise. The neural network will train under noisy observations,\nimplying that it recognizes the optimized updating rule without true labels. A\nsingle-headed attention recurrent neural network is devised with learnable\nfeature embedding to update the adaptive filter weight efficiently, enabling\naccurate computation of the secondary source to attenuate the unwanted primary\nnoise. In order to relax the time constraint on updating the adaptive filter\nweights, the delayless subband architecture is employed, which will allow the\nsystem to be updated less frequently as the downsampling factor increases. In\naddition, the delayless subband architecture does not introduce additional time\ndelays in active noise control systems. A skip updating strategy is introduced\nto decrease the updating frequency further so that machines with limited\nresources have more possibility to board our meta-learning-based model.\nExtensive multi-condition training ensures generalization and robustness\nagainst various types of noise and environments. Simulation results demonstrate\nthat our meta-learning-based model achieves superior noise reduction\nperformance compared to traditional methods.\n", "date": "2024-12-27", "categories": []}
{"id": "2412.18217", "title": "U-Mamba-Net: A highly efficient Mamba-based U-net style network for\n  noisy and reverberant speech separation", "abstract": "  The topic of speech separation involves separating mixed speech with multiple\noverlapping speakers into several streams, with each stream containing speech\nfrom only one speaker. Many highly effective models have emerged and\nproliferated rapidly over time. However, the size and computational load of\nthese models have also increased accordingly. This is a disaster for the\ncommunity, as researchers need more time and computational resources to\nreproduce and compare existing models. In this paper, we propose U-mamba-net: a\nlightweight Mamba-based U-style model for speech separation in complex\nenvironments. Mamba is a state space sequence model that incorporates feature\nselection capabilities. U-style network is a fully convolutional neural network\nwhose symmetric contracting and expansive paths are able to learn\nmulti-resolution features. In our work, Mamba serves as a feature filter,\nalternating with U-Net. We test the proposed model on Libri2mix. The results\nshow that U-Mamba-Net achieves improved performance with quite low\ncomputational cost.\n", "date": "2024-12-24", "categories": []}
{"id": "2206.08297", "title": "A Language Model With Million Context Length For Raw Audio", "abstract": "  Modeling long-term dependencies for audio signals is a particularly\nchallenging problem, as even small-time scales yield on the order of a hundred\nthousand samples. With the recent advent of Transformers, neural architectures\nbecame good at modeling dependencies over longer time scales, but they suffered\nfrom quadratic constraints to scale them. We propose a generative\nauto-regressive architecture that can model audio waveforms over quite a large\ncontext, greater than 500,000 samples. Our work is adapted to learn time\ndependencies by learning a latent representation by a CNN front-end, and then\nlearning dependencies over these representations using Transformer encoders,\nfully trained end-to-end: thereby allowing to learn representations as it deems\nfit for the next sample. Unlike previous works that compared different time\nscales to show improvement, we use a standard dataset, with the same number of\nparameters/context to show improvements. We achieve a state-of-the-art\nperformance as compared to other approaches such as Wavenet, SaSHMI, and\nSample-RNN on a standard dataset for modeling long-term structure. This work\ngives very exciting direction for the field, given improvements in context\nmodeling that can be scaled with more data, as well as potentially better\nresults by using billions/trillions of parameters.\n", "date": "2022-06-16", "categories": []}
{"id": "2303.10446", "title": "Content Adaptive Front End For Audio Classification", "abstract": "  We propose a learnable content adaptive front end for audio signal\nprocessing. Before the modern advent of deep learning, we used fixed\nrepresentation non-learnable front-ends like spectrogram or mel-spectrogram\nwith/without neural architectures. With convolutional architectures supporting\nvarious applications such as ASR and acoustic scene understanding, a shift to a\nlearnable front ends occurred in which both the type of basis functions and the\nweight were learned from scratch and optimized for the particular task of\ninterest. With the shift to transformer-based architectures with no\nconvolutional blocks present, a linear layer projects small waveform patches\nonto a small latent dimension before feeding them to a transformer\narchitecture. In this work, we propose a way of computing a content-adaptive\nlearnable time-frequency representation. We pass each audio signal through a\nbank of convolutional filters, each giving a fixed-dimensional vector. It is\nakin to learning a bank of finite impulse-response filterbanks and passing the\ninput signal through the optimum filter bank depending on the content of the\ninput signal. A content-adaptive learnable time-frequency representation may be\nmore broadly applicable, beyond the experiments in this paper.\n", "date": "2023-03-18", "categories": []}
{"id": "2311.02369", "title": "TACNET: Temporal Audio Source Counting Network", "abstract": "  In this paper, we introduce the Temporal Audio Source Counting Network\n(TaCNet), an innovative architecture that addresses limitations in audio source\ncounting tasks. TaCNet operates directly on raw audio inputs, eliminating\ncomplex preprocessing steps and simplifying the workflow. Notably, it excels in\nreal-time speaker counting, even with truncated input windows. Our extensive\nevaluation, conducted using the LibriCount dataset, underscores TaCNet's\nexceptional performance, positioning it as a state-of-the-art solution for\naudio source counting tasks. With an average accuracy of 74.18 percentage over\n11 classes, TaCNet demonstrates its effectiveness across diverse scenarios,\nincluding applications involving Chinese and Persian languages. This\ncross-lingual adaptability highlights its versatility and potential impact.\n", "date": "2023-11-04", "categories": []}
{"id": "2408.15771", "title": "wav2pos: Sound Source Localization using Masked Autoencoders", "abstract": "  We present a novel approach to the 3D sound source localization task for\ndistributed ad-hoc microphone arrays by formulating it as a set-to-set\nregression problem. By training a multi-modal masked autoencoder model that\noperates on audio recordings and microphone coordinates, we show that such a\nformulation allows for accurate localization of the sound source, by\nreconstructing coordinates masked in the input. Our approach is flexible in the\nsense that a single model can be used with an arbitrary number of microphones,\neven when a subset of audio recordings and microphone coordinates are missing.\nWe test our method on simulated and real-world recordings of music and speech\nin indoor environments, and demonstrate competitive performance compared to\nboth classical and other learning based localization methods.\n", "date": "2024-08-28", "categories": []}
{"id": "2410.02144", "title": "SoundMorpher: Perceptually-Uniform Sound Morphing with Diffusion Model", "abstract": "  We present SoundMorpher, an open-world sound morphing method designed to\ngenerate perceptually uniform morphing trajectories. Traditional sound morphing\ntechniques typically assume a linear relationship between the morphing factor\nand sound perception, achieving smooth transitions by linearly interpolating\nthe semantic features of source and target sounds while gradually adjusting the\nmorphing factor. However, these methods oversimplify the complexities of sound\nperception, resulting in limitations in morphing quality. In contrast,\nSoundMorpher explores an explicit relationship between the morphing factor and\nthe perception of morphed sounds, leveraging log Mel-spectrogram features. This\napproach further refines the morphing sequence by ensuring a constant target\nperceptual difference for each transition and determining the corresponding\nmorphing factors using binary search. To address the lack of a formal\nquantitative evaluation framework for sound morphing, we propose a set of\nmetrics based on three established objective criteria. These metrics enable\ncomprehensive assessment of morphed results and facilitate direct comparisons\nbetween methods, fostering advancements in sound morphing research. Extensive\nexperiments demonstrate the effectiveness and versatility of SoundMorpher in\nreal-world scenarios, showcasing its potential in applications such as creative\nmusic composition, film post-production, and interactive audio technologies.\nOur demonstration and codes are available\nat~\\url{https://xinleiniu.github.io/SoundMorpher-demo/}.\n", "date": "2024-10-03", "categories": []}
{"id": "2412.11769", "title": "Does it Chug? Towards a Data-Driven Understanding of Guitar Tone\n  Description", "abstract": "  Natural language is commonly used to describe instrument timbre, such as a\n\"warm\" or \"heavy\" sound. As these descriptors are based on human perception,\nthere can be disagreement over which acoustic features correspond to a given\nadjective. In this work, we pursue a data-driven approach to further our\nunderstanding of such adjectives in the context of guitar tone. Our main\ncontribution is a dataset of timbre adjectives, constructed by processing\nsingle clips of instrument audio to produce varied timbres through adjustments\nin EQ and effects such as distortion. Adjective annotations are obtained for\neach clip by crowdsourcing experts to complete a pairwise comparison and a\nlabeling task. We examine the dataset and reveal correlations between adjective\nratings and highlight instances where the data contradicts prevailing theories\non spectral features and timbral adjectives, suggesting a need for a more\nnuanced, data-driven understanding of timbre.\n", "date": "2024-12-16", "categories": []}
{"id": "2411.12008", "title": "Compression of Higher Order Ambisonics with Multichannel RVQGAN", "abstract": "  A multichannel extension to the RVQGAN neural coding method is proposed, and\nrealized for data-driven compression of third-order Ambisonics audio. The\ninput- and output layers of the generator and discriminator models are modified\nto accept multiple (16) channels without increasing the model bitrate. We also\npropose a loss function for accounting for spatial perception in immersive\nreproduction, and transfer learning from single-channel models. Listening test\nresults with 7.1.4 immersive playback show that the proposed extension is\nsuitable for coding scene-based, 16-channel Ambisonics content with good\nquality at 16 kbps when trained and tested on the EigenScape database. The\nmodel has potential applications for learning other types of content and\nmultichannel formats.\n", "date": "2024-11-18", "categories": []}
{"id": "2412.06703", "title": "Source Separation & Automatic Transcription for Music", "abstract": "  Source separation is the process of isolating individual sounds in an\nauditory mixture of multiple sounds [1], and has a variety of applications\nranging from speech enhancement and lyric transcription [2] to digital audio\nproduction for music. Furthermore, Automatic Music Transcription (AMT) is the\nprocess of converting raw music audio into sheet music that musicians can read\n[3]. Historically, these tasks have faced challenges such as significant audio\nnoise, long training times, and lack of free-use data due to copyright\nrestrictions. However, recent developments in deep learning have brought new\npromising approaches to building low-distortion stems and generating sheet\nmusic from audio signals [4]. Using spectrogram masking, deep neural networks,\nand the MuseScore API, we attempt to create an end-to-end pipeline that allows\nfor an initial music audio mixture (e.g...wav file) to be separated into\ninstrument stems, converted into MIDI files, and transcribed into sheet music\nfor each component instrument.\n", "date": "2024-12-09", "categories": []}
{"id": "2308.05037", "title": "Separate Anything You Describe", "abstract": "  Language-queried audio source separation (LASS) is a new paradigm for\ncomputational auditory scene analysis (CASA). LASS aims to separate a target\nsound from an audio mixture given a natural language query, which provides a\nnatural and scalable interface for digital audio applications. Recent works on\nLASS, despite attaining promising separation performance on specific sources\n(e.g., musical instruments, limited classes of audio events), are unable to\nseparate audio concepts in the open domain. In this work, we introduce\nAudioSep, a foundation model for open-domain audio source separation with\nnatural language queries. We train AudioSep on large-scale multimodal datasets\nand extensively evaluate its capabilities on numerous tasks including audio\nevent separation, musical instrument separation, and speech enhancement.\nAudioSep demonstrates strong separation performance and impressive zero-shot\ngeneralization ability using audio captions or text labels as queries,\nsubstantially outperforming previous audio-queried and language-queried sound\nseparation models. For reproducibility of this work, we will release the source\ncode, evaluation benchmark and pre-trained model at:\nhttps://github.com/Audio-AGI/AudioSep.\n", "date": "2023-08-09", "categories": []}
{"id": "2405.00233", "title": "SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General\n  Sound", "abstract": "  Large language models (LLMs) have significantly advanced audio processing\nthrough audio codecs that convert audio into discrete tokens, enabling the\napplication of language modelling techniques to audio data. However,\ntraditional codecs often operate at high bitrates or within narrow domains such\nas speech and lack the semantic clues required for efficient language\nmodelling. Addressing these challenges, we introduce SemantiCodec, a novel\ncodec designed to compress audio into fewer than a hundred tokens per second\nacross diverse audio types, including speech, general sound, and music, without\ncompromising quality. SemantiCodec features a dual-encoder architecture: a\nsemantic encoder using a self-supervised pre-trained Audio Masked Autoencoder\n(AudioMAE), discretized using k-means clustering on extensive audio data, and\nan acoustic encoder to capture the remaining details. The semantic and acoustic\nencoder outputs are used to reconstruct audio via a diffusion-model-based\ndecoder. SemantiCodec is presented in three variants with token rates of 25,\n50, and 100 per second, supporting a range of ultra-low bit rates between 0.31\nkbps and 1.40 kbps. Experimental results demonstrate that SemantiCodec\nsignificantly outperforms the state-of-the-art Descript codec on reconstruction\nquality. Our results also suggest that SemantiCodec contains significantly\nricher semantic information than all evaluated state-of-the-art audio codecs,\neven at significantly lower bitrates. Our code and demos are available at\nhttps://haoheliu.github.io/SemantiCodec/.\n", "date": "2024-04-30", "categories": []}
{"id": "2411.18636", "title": "Towards Advanced Speech Signal Processing: A Statistical Perspective on\n  Convolution-Based Architectures and its Applications", "abstract": "  This article surveys convolution-based models including convolutional neural\nnetworks (CNNs), Conformers, ResNets, and CRNNs-as speech signal processing\nmodels and provide their statistical backgrounds and speech recognition,\nspeaker identification, emotion recognition, and speech enhancement\napplications. Through comparative training cost assessment, model size,\naccuracy and speed assessment, we compare the strengths and weaknesses of each\nmodel, identify potential errors and propose avenues for further research,\nemphasizing the central role it plays in advancing applications of speech\ntechnologies.\n", "date": "2024-11-20", "categories": []}
{"id": "2406.11248", "title": "Performance Improvement of Language-Queried Audio Source Separation\n  Based on Caption Augmentation From Large Language Models for DCASE Challenge\n  2024 Task 9", "abstract": "  We present a prompt-engineering-based text-augmentation approach applied to a\nlanguage-queried audio source separation (LASS) task. To enhance the\nperformance of LASS, the proposed approach utilizes large language models\n(LLMs) to generate multiple captions corresponding to each sentence of the\ntraining dataset. To this end, we first perform experiments to identify the\nmost effective prompts for caption augmentation with a smaller number of\ncaptions. A LASS model trained with these augmented captions demonstrates\nimproved performance on the DCASE 2024 Task 9 validation set compared to that\ntrained without augmentation. This study highlights the effectiveness of\nLLM-based caption augmentation in advancing language-queried audio source\nseparation.\n", "date": "2024-06-17", "categories": []}
{"id": "2411.18497", "title": "Multiple Choice Learning for Efficient Speech Separation with Many\n  Speakers", "abstract": "  Training speech separation models in the supervised setting raises a\npermutation problem: finding the best assignation between the model predictions\nand the ground truth separated signals. This inherently ambiguous task is\ncustomarily solved using Permutation Invariant Training (PIT). In this article,\nwe instead consider using the Multiple Choice Learning (MCL) framework, which\nwas originally introduced to tackle ambiguous tasks. We demonstrate\nexperimentally on the popular WSJ0-mix and LibriMix benchmarks that MCL matches\nthe performances of PIT, while being computationally advantageous. This opens\nthe door to a promising research direction, as MCL can be naturally extended to\nhandle a variable number of speakers, or to tackle speech separation in the\nunsupervised setting.\n", "date": "2024-11-27", "categories": []}
{"id": "2411.13179", "title": "SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio", "abstract": "  Time delay estimation or Time-Difference-Of-Arrival estimates is a critical\ncomponent for multiple localization applications such as multilateration,\ndirection of arrival, and self-calibration. The task is to estimate the time\ndifference between a signal arriving at two different sensors. For the audio\nsensor modality, most current systems are based on classical methods such as\nthe Generalized Cross-Correlation Phase Transform (GCC-PHAT) method. In this\npaper we demonstrate that learning based methods can, even based on synthetic\ndata, significantly outperform GCC-PHAT on novel real world data. To overcome\nthe lack of data with ground truth for the task, we train our model on a\nsimulated dataset which is sufficiently large and varied, and that captures the\nrelevant characteristics of the real world problem. We provide our trained\nmodel, SONNET (Simulation Optimized Neural Network Estimator of Timeshifts),\nwhich is runnable in real-time and works on novel data out of the box for many\nreal data applications, i.e. without re-training. We further demonstrate\ngreatly improved performance on the downstream task of self-calibration when\nusing our model compared to classical methods.\n", "date": "2024-11-20", "categories": []}
{"id": "2411.11312", "title": "Study of the Performance of CEEMDAN in Underdetermined Speech Separation", "abstract": "  The CEEMDAN algorithm is one of the modern methods used in the analysis of\nnon-stationary signals. This research presents a study of the effectiveness of\nthis method in audio source separation to know the limits of its work. It\nconcluded two conditions related to frequencies and amplitudes of mixed signals\nto be separated by CEEMDAN. The performance of the algorithm in separating\nnoise from speech and separating speech signals from each other is studied. The\nresearch reached a conclusion that CEEMDAN can remove some types of noise from\nspeech (speech improvement), and it cannot separate speech signals from each\nother (cocktail party). Simulation is done using Matlab environment and Noizeus\ndatabase.\n", "date": "2024-11-18", "categories": []}
{"id": "2411.08375", "title": "Developing an Effective Training Dataset to Enhance the Performance of\n  AI-based Speaker Separation Systems", "abstract": "  This paper addresses the challenge of speaker separation, which remains an\nactive research topic despite the promising results achieved in recent years.\nThese results, however, often degrade in real recording conditions due to the\npresence of noise, echo, and other interferences. This is because neural models\nare typically trained on synthetic datasets consisting of mixed audio signals\nand their corresponding ground truths, which are generated using computer\nsoftware and do not fully represent the complexities of real-world recording\nscenarios. The lack of realistic training sets for speaker separation remains a\nmajor hurdle, as obtaining individual sounds from mixed audio signals is a\nnontrivial task. To address this issue, we propose a novel method for\nconstructing a realistic training set that includes mixture signals and\ncorresponding ground truths for each speaker. We evaluate this dataset on a\ndeep learning model and compare it to a synthetic dataset. We got a 1.65 dB\nimprovement in Scale Invariant Signal to Distortion Ratio (SI-SDR) for speaker\nseparation accuracy in realistic mixing. Our findings highlight the potential\nof realistic training sets for enhancing the performance of speaker separation\nmodels in real-world scenarios.\n", "date": "2024-11-13", "categories": []}
{"id": "2402.09821", "title": "Diffusion Models for Audio Restoration", "abstract": "  With the development of audio playback devices and fast data transmission,\nthe demand for high sound quality is rising for both entertainment and\ncommunications. In this quest for better sound quality, challenges emerge from\ndistortions and interferences originating at the recording side or caused by an\nimperfect transmission pipeline. To address this problem, audio restoration\nmethods aim to recover clean sound signals from the corrupted input data. We\npresent here audio restoration algorithms based on diffusion models, with a\nfocus on speech enhancement and music restoration tasks. Traditional\napproaches, often grounded in handcrafted rules and statistical heuristics,\nhave shaped our understanding of audio signals. In the past decades, there has\nbeen a notable shift towards data-driven methods that exploit the modeling\ncapabilities of DNNs. Deep generative models, and among them diffusion models,\nhave emerged as powerful techniques for learning complex data distributions.\nHowever, relying solely on DNN-based learning approaches carries the risk of\nreducing interpretability, particularly when employing end-to-end models.\nNonetheless, data-driven approaches allow more flexibility in comparison to\nstatistical model-based frameworks, whose performance depends on distributional\nand statistical assumptions that can be difficult to guarantee. Here, we aim to\nshow that diffusion models can combine the best of both worlds and offer the\nopportunity to design audio restoration algorithms with a good degree of\ninterpretability and a remarkable performance in terms of sound quality. We\nexplain the diffusion formalism and its application to the conditional\ngeneration of clean audio signals. We believe that diffusion models open an\nexciting field of research with the potential to spawn new audio restoration\nalgorithms that are natural-sounding and remain robust in difficult acoustic\nsituations.\n", "date": "2024-02-15", "categories": []}
{"id": "2407.11745", "title": "Universal Sound Separation with Self-Supervised Audio Masked Autoencoder", "abstract": "  Universal sound separation (USS) is a task of separating mixtures of\narbitrary sound sources. Typically, universal separation models are trained\nfrom scratch in a supervised manner, using labeled data. Self-supervised\nlearning (SSL) is an emerging deep learning approach that leverages unlabeled\ndata to obtain task-agnostic representations, which can benefit many downstream\ntasks. In this paper, we propose integrating a self-supervised pre-trained\nmodel, namely the audio masked autoencoder (A-MAE), into a universal sound\nseparation system to enhance its separation performance. We employ two\nstrategies to utilize SSL embeddings: freezing or updating the parameters of\nA-MAE during fine-tuning. The SSL embeddings are concatenated with the\nshort-time Fourier transform (STFT) to serve as input features for the\nseparation model. We evaluate our methods on the AudioSet dataset, and the\nexperimental results indicate that the proposed methods successfully enhance\nthe separation performance of a state-of-the-art ResUNet-based USS model.\n", "date": "2024-07-16", "categories": []}
{"id": "2411.02787", "title": "Advancing Robust Underwater Acoustic Target Recognition through\n  Multi-task Learning and Multi-Gate Mixture-of-Experts", "abstract": "  Underwater acoustic target recognition has emerged as a prominent research\narea within the field of underwater acoustics. However, the current\navailability of authentic underwater acoustic signal recordings remains\nlimited, which hinders data-driven acoustic recognition models from learning\nrobust patterns of targets from a limited set of intricate underwater signals,\nthereby compromising their stability in practical applications. To overcome\nthese limitations, this study proposes a recognition framework called M3\n(Multi-task, Multi-gate, Multi-expert) to enhance the model's ability to\ncapture robust patterns by making it aware of the inherent properties of\ntargets. In this framework, an auxiliary task that focuses on target\nproperties, such as estimating target size, is designed. The auxiliary task\nthen shares parameters with the recognition task to realize multi-task\nlearning. This paradigm allows the model to concentrate on shared information\nacross tasks and identify robust patterns of targets in a regularized manner,\nthereby enhancing the model's generalization ability. Moreover, M3 incorporates\nmulti-expert and multi-gate mechanisms, allowing for the allocation of distinct\nparameter spaces to various underwater signals. This enables the model to\nprocess intricate signal patterns in a fine-grained and differentiated manner.\nTo evaluate the effectiveness of M3, extensive experiments were implemented on\nthe ShipsEar underwater ship-radiated noise dataset. The results substantiate\nthat M3 has the ability to outperform the most advanced single-task recognition\nmodels, thereby achieving the state-of-the-art performance.\n", "date": "2024-11-05", "categories": []}
{"id": "2411.02860", "title": "Continual Audio-Visual Sound Separation", "abstract": "  In this paper, we introduce a novel continual audio-visual sound separation\ntask, aiming to continuously separate sound sources for new classes while\npreserving performance on previously learned classes, with the aid of visual\nguidance. This problem is crucial for practical visually guided auditory\nperception as it can significantly enhance the adaptability and robustness of\naudio-visual sound separation models, making them more applicable for\nreal-world scenarios where encountering new sound sources is commonplace. The\ntask is inherently challenging as our models must not only effectively utilize\ninformation from both modalities in current tasks but also preserve their\ncross-modal association in old tasks to mitigate catastrophic forgetting during\naudio-visual continual learning. To address these challenges, we propose a\nnovel approach named ContAV-Sep (\\textbf{Cont}inual\n\\textbf{A}udio-\\textbf{V}isual Sound \\textbf{Sep}aration). ContAV-Sep presents\na novel Cross-modal Similarity Distillation Constraint (CrossSDC) to uphold the\ncross-modal semantic similarity through incremental tasks and retain previously\nacquired knowledge of semantic similarity in old models, mitigating the risk of\ncatastrophic forgetting. The CrossSDC can seamlessly integrate into the\ntraining process of different audio-visual sound separation frameworks.\nExperiments demonstrate that ContAV-Sep can effectively mitigate catastrophic\nforgetting and achieve significantly better performance compared to other\ncontinual learning baselines for audio-visual sound separation. Code is\navailable at: \\url{https://github.com/weiguoPian/ContAV-Sep_NeurIPS2024}.\n", "date": "2024-11-05", "categories": []}
{"id": "2411.03085", "title": "Speech Separation with Pretrained Frontend to Minimize Domain Mismatch", "abstract": "  Speech separation seeks to separate individual speech signals from a speech\nmixture. Typically, most separation models are trained on synthetic data due to\nthe unavailability of target reference in real-world cocktail party scenarios.\nAs a result, there exists a domain gap between real and synthetic data when\ndeploying speech separation models in real-world applications. In this paper,\nwe propose a self-supervised domain-invariant pretrained (DIP) frontend that is\nexposed to mixture data without the need for target reference speech. The DIP\nfrontend utilizes a Siamese network with two innovative pretext tasks, mixture\npredictive coding (MPC) and mixture invariant coding (MIC), to capture shared\ncontextual cues between real and synthetic unlabeled mixtures. Subsequently, we\nfreeze the DIP frontend as a feature extractor when training the downstream\nspeech separation models on synthetic data. By pretraining the DIP frontend\nwith the contextual cues, we expect that the speech separation skills learned\nfrom synthetic data can be effectively transferred to real data. To benefit\nfrom the DIP frontend, we introduce a novel separation pipeline to align the\nfeature resolution of the separation models. We evaluate the speech separation\nquality on standard benchmarks and real-world datasets. The results confirm the\nsuperiority of our DIP frontend over existing speech separation models. This\nstudy underscores the potential of large-scale pretraining to enhance the\nquality and intelligibility of speech separation in real-world applications.\n", "date": "2024-11-05", "categories": []}
{"id": "2410.23796", "title": "Improving snore detection under limited dataset through\n  harmonic/percussive source separation and convolutional neural networks", "abstract": "  Snoring, an acoustic biomarker commonly observed in individuals with\nObstructive Sleep Apnoea Syndrome (OSAS), holds significant potential for\ndiagnosing and monitoring this recognized clinical disorder. Irrespective of\nsnoring types, most snoring instances exhibit identifiable harmonic patterns\nmanifested through distinctive energy distributions over time. In this work, we\npropose a novel method to differentiate monaural snoring from non-snoring\nsounds by analyzing the harmonic content of the input sound using\nharmonic/percussive sound source separation (HPSS). The resulting feature,\nbased on the harmonic spectrogram from HPSS, is employed as input data for\nconventional neural network architectures, aiming to enhance snoring detection\nperformance even under a limited data learning framework. To evaluate the\nperformance of our proposal, we studied two different scenarios: 1) using a\nlarge dataset of snoring and interfering sounds, and 2) using a reduced\ntraining set composed of around 1% of the data material. In the former\nscenario, the proposed HPSS-based feature provides competitive results compared\nto other input features from the literature. However, the key advantage of the\nproposed method lies in the superior performance of the harmonic spectrogram\nderived from HPSS in a limited data learning context. In this particular\nscenario, using the proposed harmonic feature significantly enhances the\nperformance of all the studied architectures in comparison to the classical\ninput features documented in the existing literature. This finding clearly\ndemonstrates that incorporating harmonic content enables more reliable learning\nof the essential time-frequency characteristics that are prevalent in most\nsnoring sounds, even in scenarios where the amount of training data is limited.\n", "date": "2024-10-31", "categories": []}
{"id": "2410.21557", "title": "A Novel Score-CAM based Denoiser for Spectrographic Signature Extraction\n  without Ground Truth", "abstract": "  Sonar based audio classification techniques are a growing area of research in\nthe field of underwater acoustics. Usually, underwater noise picked up by\npassive sonar transducers contains all types of signals that travel through the\nocean and is transformed into spectrographic images. As a result, the\ncorresponding spectrograms intended to display the temporal-frequency data of a\ncertain object often include the tonal regions of abundant extraneous noise\nthat can effectively interfere with a 'contact'. So, a majority of\nspectrographic samples extracted from underwater audio signals are rendered\nunusable due to their clutter and lack the required indistinguishability\nbetween different objects. With limited clean true data for supervised\ntraining, creating classification models for these audio signals is severely\nbottlenecked.\n  This paper derives several new techniques to combat this problem by\ndeveloping a novel Score-CAM based denoiser to extract an object's signature\nfrom noisy spectrographic data without being given any ground truth data. In\nparticular, this paper proposes a novel generative adversarial network\narchitecture for learning and producing spectrographic training data in similar\ndistributions to low-feature spectrogram inputs. In addition, this paper also a\ngeneralizable class activation mapping based denoiser for different\ndistributions of acoustic data, even real-world data distributions. Utilizing\nthese novel architectures and proposed denoising techniques, these experiments\ndemonstrate state-of-the-art noise reduction accuracy and improved\nclassification accuracy than current audio classification standards. As such,\nthis approach has applications not only to audio data but for countless data\ndistributions used all around the world for machine learning.\n", "date": "2024-10-28", "categories": []}
{"id": "2410.21561", "title": "Audio Classification of Low Feature Spectrograms Utilizing Convolutional\n  Neural Networks", "abstract": "  Modern day audio signal classification techniques lack the ability to\nclassify low feature audio signals in the form of spectrographic temporal\nfrequency data representations. Additionally, currently utilized techniques\nrely on full diverse data sets that are often not representative of real-world\ndistributions. This paper derives several first-of-its-kind machine learning\nmethodologies to analyze these low feature audio spectrograms given data\ndistributions that may have normalized, skewed, or even limited training sets.\nIn particular, this paper proposes several novel customized convolutional\narchitectures to extract identifying features using binary, one-class, and\nsiamese approaches to identify the spectrographic signature of a given audio\nsignal. Utilizing these novel convolutional architectures as well as the\nproposed classification methods, these experiments demonstrate state-of-the-art\nclassification accuracy and improved efficiency than traditional audio\nclassification methods.\n", "date": "2024-10-28", "categories": []}
{"id": "2410.20773", "title": "An Ensemble Approach to Music Source Separation: A Comparative Analysis\n  of Conventional and Hierarchical Stem Separation", "abstract": "  Music source separation (MSS) is a task that involves isolating individual\nsound sources, or stems, from mixed audio signals. This paper presents an\nensemble approach to MSS, combining several state-of-the-art architectures to\nachieve superior separation performance across traditional Vocal, Drum, and\nBass (VDB) stems, as well as expanding into second-level hierarchical\nseparation for sub-stems like kick, snare, lead vocals, and background vocals.\nOur method addresses the limitations of relying on a single model by utilising\nthe complementary strengths of various models, leading to more balanced results\nacross stems. For stem selection, we used the harmonic mean of Signal-to-Noise\nRatio (SNR) and Signal-to-Distortion Ratio (SDR), ensuring that extreme values\ndo not skew the results and that both metrics are weighted effectively. In\naddition to consistently high performance across the VDB stems, we also\nexplored second-level hierarchical separation, revealing important insights\ninto the complexities of MSS and how factors like genre and instrumentation can\ninfluence model performance. While the second-level separation results show\nroom for improvement, the ability to isolate sub-stems marks a significant\nadvancement. Our findings pave the way for further research in MSS,\nparticularly in expanding model capabilities beyond VDB and improving niche\nstem separations such as guitar and piano.\n", "date": "2024-10-28", "categories": []}
{"id": "2410.20997", "title": "SepMamba: State-space models for speaker separation using Mamba", "abstract": "  Deep learning-based single-channel speaker separation has improved\nsignificantly in recent years largely due to the introduction of the\ntransformer-based attention mechanism. However, these improvements come at the\nexpense of intense computational demands, precluding their use in many\npractical applications. As a computationally efficient alternative with similar\nmodeling capabilities, Mamba was recently introduced. We propose SepMamba, a\nU-Net-based architecture composed primarily of bidirectional Mamba layers. We\nfind that our approach outperforms similarly-sized prominent models - including\ntransformer-based models - on the WSJ0 2-speaker dataset while enjoying a\nsignificant reduction in computational cost, memory usage, and forward pass\ntime. We additionally report strong results for causal variants of SepMamba.\nOur approach provides a computationally favorable alternative to\ntransformer-based architectures for deep speech separation.\n", "date": "2024-10-28", "categories": []}
{"id": "2410.21269", "title": "OmniSep: Unified Omni-Modality Sound Separation with Query-Mixup", "abstract": "  The scaling up has brought tremendous success in the fields of vision and\nlanguage in recent years. When it comes to audio, however, researchers\nencounter a major challenge in scaling up the training data, as most natural\naudio contains diverse interfering signals. To address this limitation, we\nintroduce Omni-modal Sound Separation (OmniSep), a novel framework capable of\nisolating clean soundtracks based on omni-modal queries, encompassing both\nsingle-modal and multi-modal composed queries. Specifically, we introduce the\nQuery-Mixup strategy, which blends query features from different modalities\nduring training. This enables OmniSep to optimize multiple modalities\nconcurrently, effectively bringing all modalities under a unified framework for\nsound separation. We further enhance this flexibility by allowing queries to\ninfluence sound separation positively or negatively, facilitating the retention\nor removal of specific sounds as desired. Finally, OmniSep employs a\nretrieval-augmented approach known as Query-Aug, which enables open-vocabulary\nsound separation. Experimental evaluations on MUSIC, VGGSOUND-CLEAN+, and\nMUSIC-CLEAN+ datasets demonstrate effectiveness of OmniSep, achieving\nstate-of-the-art performance in text-, image-, and audio-queried sound\nseparation tasks. For samples and further information, please visit the demo\npage at \\url{https://omnisep.github.io/}.\n", "date": "2024-10-28", "categories": []}
{"id": "2404.07970", "title": "Differentiable All-pole Filters for Time-varying Audio Systems", "abstract": "  Infinite impulse response filters are an essential building block of many\ntime-varying audio systems, such as audio effects and synthesisers. However,\ntheir recursive structure impedes end-to-end training of these systems using\nautomatic differentiation. Although non-recursive filter approximations like\nfrequency sampling and frame-based processing have been proposed and widely\nused in previous works, they cannot accurately reflect the gradient of the\noriginal system. We alleviate this difficulty by re-expressing a time-varying\nall-pole filter to backpropagate the gradients through itself, so the filter\nimplementation is not bound to the technical limitations of automatic\ndifferentiation frameworks. This implementation can be employed within audio\nsystems containing filters with poles for efficient gradient evaluation. We\ndemonstrate its training efficiency and expressive capabilities for modelling\nreal-world dynamic audio systems on a phaser, time-varying subtractive\nsynthesiser, and compressor. We make our code and audio samples available and\nprovide the trained audio effect and synth models in a VST plugin at\nhttps://diffapf.github.io/web/.\n", "date": "2024-04-11", "categories": []}
{"id": "2410.14411", "title": "SNAC: Multi-Scale Neural Audio Codec", "abstract": "  Neural audio codecs have recently gained popularity because they can\nrepresent audio signals with high fidelity at very low bitrates, making it\nfeasible to use language modeling approaches for audio generation and\nunderstanding. Residual Vector Quantization (RVQ) has become the standard\ntechnique for neural audio compression using a cascade of VQ codebooks. This\npaper proposes the Multi-Scale Neural Audio Codec, a simple extension of RVQ\nwhere the quantizers can operate at different temporal resolutions. By applying\na hierarchy of quantizers at variable frame rates, the codec adapts to the\naudio structure across multiple timescales. This leads to more efficient\ncompression, as demonstrated by extensive objective and subjective evaluations.\nThe code and model weights are open-sourced at\nhttps://github.com/hubertsiuzdak/snac.\n", "date": "2024-10-18", "categories": []}
{"id": "2308.00122", "title": "High-Quality Visually-Guided Sound Separation from Diverse Categories", "abstract": "  We propose DAVIS, a Diffusion-based Audio-VIsual Separation framework that\nsolves the audio-visual sound source separation task through generative\nlearning. Existing methods typically frame sound separation as a mask-based\nregression problem, achieving significant progress. However, they face\nlimitations in capturing the complex data distribution required for\nhigh-quality separation of sounds from diverse categories. In contrast, DAVIS\nleverages a generative diffusion model and a Separation U-Net to synthesize\nseparated sounds directly from Gaussian noise, conditioned on both the audio\nmixture and the visual information. With its generative objective, DAVIS is\nbetter suited to achieving the goal of high-quality sound separation across\ndiverse sound categories. We compare DAVIS to existing state-of-the-art\ndiscriminative audio-visual separation methods on the AVE and MUSIC datasets,\nand results show that DAVIS outperforms other methods in separation quality,\ndemonstrating the advantages of our framework for tackling the audio-visual\nsource separation task.\n", "date": "2023-07-31", "categories": []}
{"id": "2305.10652", "title": "Speech Separation based on Contrastive Learning and Deep Modularization", "abstract": "  The current monaural state of the art tools for speech separation relies on\nsupervised learning. This means that they must deal with permutation problem,\nthey are impacted by the mismatch on the number of speakers used in training\nand inference. Moreover, their performance heavily relies on the presence of\nhigh-quality labelled data. These problems can be effectively addressed by\nemploying a fully unsupervised technique for speech separation. In this paper,\nwe use contrastive learning to establish the representations of frames then use\nthe learned representations in the downstream deep modularization task.\nConcretely, we demonstrate experimentally that in speech separation, different\nframes of a speaker can be viewed as augmentations of a given hidden standard\nframe of that speaker. The frames of a speaker contain enough prosodic\ninformation overlap which is key in speech separation. Based on this, we\nimplement a self-supervised learning to learn to minimize the distance between\nframes belonging to a given speaker. The learned representations are used in a\ndownstream deep modularization task to cluster frames based on speaker\nidentity. Evaluation of the developed technique on WSJ0-2mix and WSJ0-3mix\nshows that the technique attains SI-SNRi and SDRi of 20.8 and 21.0 respectively\nin WSJ0-2mix. In WSJ0-3mix, it attains SI-SNRi and SDRi of 20.7 and 20.7\nrespectively in WSJ0-2mix. Its greatest strength being that as the number of\nspeakers increase, its performance does not degrade significantly.\n", "date": "2023-05-18", "categories": []}
{"id": "2404.00082", "title": "Data-Driven Room Acoustic Modeling Via Differentiable Feedback Delay\n  Networks With Learnable Delay Lines", "abstract": "  Over the past few decades, extensive research has been devoted to the design\nof artificial reverberation algorithms aimed at emulating the room acoustics of\nphysical environments. Despite significant advancements, automatic parameter\ntuning of delay-network models remains an open challenge. We introduce a novel\nmethod for finding the parameters of a Feedback Delay Network (FDN) such that\nits output renders target attributes of a measured room impulse response. The\nproposed approach involves the implementation of a differentiable FDN with\ntrainable delay lines, which, for the first time, allows us to simultaneously\nlearn each and every delay-network parameter via backpropagation. The iterative\noptimization process seeks to minimize a perceptually-motivated time-domain\nloss function incorporating differentiable terms accounting for energy decay\nand echo density. Through experimental validation, we show that the proposed\nmethod yields time-invariant frequency-independent FDNs capable of closely\nmatching the desired acoustical characteristics, and outperforms existing\nmethods based on genetic algorithms and analytical FDN design.\n", "date": "2024-03-29", "categories": []}
{"id": "2310.07284", "title": "Typing to Listen at the Cocktail Party: Text-Guided Target Speaker\n  Extraction", "abstract": "  Humans can easily isolate a single speaker from a complex acoustic\nenvironment, a capability referred to as the \"Cocktail Party Effect.\" However,\nreplicating this ability has been a significant challenge in the field of\ntarget speaker extraction (TSE). Traditional TSE approaches predominantly rely\non voiceprints, which raise privacy concerns and face issues related to the\nquality and availability of enrollment samples, as well as intra-speaker\nvariability. To address these issues, this work introduces a novel text-guided\nTSE paradigm named LLM-TSE. In this paradigm, a state-of-the-art large language\nmodel, LLaMA 2, processes typed text input from users to extract semantic cues.\nWe demonstrate that textual descriptions alone can effectively serve as cues\nfor extraction, thus addressing privacy concerns and reducing dependency on\nvoiceprints. Furthermore, our approach offers flexibility by allowing the user\nto specify the extraction or suppression of a speaker and enhances robustness\nagainst intra-speaker variability by incorporating context-dependent textual\ninformation. Experimental results show competitive performance with text-based\ncues alone and demonstrate the effectiveness of using text as a task selector.\nAdditionally, they achieve a new state-of-the-art when combining text-based\ncues with pre-registered cues. This work represents the first integration of\nLLMs with TSE, potentially establishing a new benchmark in solving the cocktail\nparty problem and expanding the scope of TSE applications by providing a\nversatile, privacy-conscious solution.\n", "date": "2023-10-11", "categories": []}
{"id": "2403.05010", "title": "RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction", "abstract": "  Recent advancements in generative modeling have significantly enhanced the\nreconstruction of audio waveforms from various representations. While diffusion\nmodels are adept at this task, they are hindered by latency issues due to their\noperation at the individual sample point level and the need for numerous\nsampling steps. In this study, we introduce RFWave, a cutting-edge multi-band\nRectified Flow approach designed to reconstruct high-fidelity audio waveforms\nfrom Mel-spectrograms or discrete acoustic tokens. RFWave uniquely generates\ncomplex spectrograms and operates at the frame level, processing all subbands\nsimultaneously to boost efficiency. Leveraging Rectified Flow, which targets a\nstraight transport trajectory, RFWave achieves reconstruction with just 10\nsampling steps. Our empirical evaluations show that RFWave not only provides\noutstanding reconstruction quality but also offers vastly superior\ncomputational efficiency, enabling audio generation at speeds up to 160 times\nfaster than real-time on a GPU. An online demonstration is available at:\nhttps://rfwave-demo.github.io/rfwave/.\n", "date": "2024-03-08", "categories": []}
{"id": "2410.04990", "title": "Stage-Wise and Prior-Aware Neural Speech Phase Prediction", "abstract": "  This paper proposes a novel Stage-wise and Prior-aware Neural Speech Phase\nPrediction (SP-NSPP) model, which predicts the phase spectrum from input\namplitude spectrum by two-stage neural networks. In the initial\nprior-construction stage, we preliminarily predict a rough prior phase spectrum\nfrom the amplitude spectrum. The subsequent refinement stage transforms the\namplitude spectrum into a refined high-quality phase spectrum conditioned on\nthe prior phase. Networks in both stages use ConvNeXt v2 blocks as the backbone\nand adopt adversarial training by innovatively introducing a phase spectrum\ndiscriminator (PSD). To further improve the continuity of the refined phase, we\nalso incorporate a time-frequency integrated difference (TFID) loss in the\nrefinement stage. Experimental results confirm that, compared to neural\nnetwork-based no-prior phase prediction methods, the proposed SP-NSPP achieves\nhigher phase prediction accuracy, thanks to introducing the coarse phase priors\nand diverse training criteria. Compared to iterative phase estimation\nalgorithms, our proposed SP-NSPP does not require multiple rounds of staged\niterations, resulting in higher generation efficiency.\n", "date": "2024-10-07", "categories": []}
{"id": "2410.01562", "title": "HRTF Estimation using a Score-based Prior", "abstract": "  We present a head-related transfer function (HRTF) estimation method which\nrelies on a data-driven prior given by a score-based diffusion model. The HRTF\nis estimated in reverberant environments using natural excitation signals, e.g.\nhuman speech. The impulse response of the room is estimated along with the HRTF\nby optimizing a parametric model of reverberation based on the statistical\nbehaviour of room acoustics. The posterior distribution of HRTF given the\nreverberant measurement and excitation signal is modelled using the score-based\nHRTF prior and a log-likelihood approximation. We show that the resulting\nmethod outperforms several baselines, including an oracle recommender system\nthat assigns the optimal HRTF in our training set based on the smallest\ndistance to the true HRTF at the given direction of arrival. In particular, we\nshow that the diffusion prior can account for the large variability of\nhigh-frequency content in HRTFs.\n", "date": "2024-10-02", "categories": []}
{"id": "2409.19270", "title": "OpenSep: Leveraging Large Language Models with Textual Inversion for\n  Open World Audio Separation", "abstract": "  Audio separation in real-world scenarios, where mixtures contain a variable\nnumber of sources, presents significant challenges due to limitations of\nexisting models, such as over-separation, under-separation, and dependence on\npredefined training sources. We propose OpenSep, a novel framework that\nleverages large language models (LLMs) for automated audio separation,\neliminating the need for manual intervention and overcoming source limitations.\nOpenSep uses textual inversion to generate captions from audio mixtures with\noff-the-shelf audio captioning models, effectively parsing the sound sources\npresent. It then employs few-shot LLM prompting to extract detailed audio\nproperties of each parsed source, facilitating separation in unseen mixtures.\nAdditionally, we introduce a multi-level extension of the mix-and-separate\ntraining framework to enhance modality alignment by separating single source\nsounds and mixtures simultaneously. Extensive experiments demonstrate OpenSep's\nsuperiority in precisely separating new, unseen, and variable sources in\nchallenging mixtures, outperforming SOTA baseline methods. Code is released at\nhttps://github.com/tanvir-utexas/OpenSep.git\n", "date": "2024-09-28", "categories": []}
{"id": "2409.18705", "title": "Speech Boosting: Low-Latency Live Speech Enhancement for TWS Earbuds", "abstract": "  This paper introduces a speech enhancement solution tailored for true\nwireless stereo (TWS) earbuds on-device usage. The solution was specifically\ndesigned to support conversations in noisy environments, with active noise\ncancellation (ANC) activated. The primary challenges for speech enhancement\nmodels in this context arise from computational complexity that limits\non-device usage and latency that must be less than 3 ms to preserve a live\nconversation. To address these issues, we evaluated several crucial design\nelements, including the network architecture and domain, design of loss\nfunctions, pruning method, and hardware-specific optimization. Consequently, we\ndemonstrated substantial improvements in speech enhancement quality compared\nwith that in baseline models, while simultaneously reducing the computational\ncomplexity and algorithmic latency.\n", "date": "2024-09-27", "categories": []}
{"id": "2407.11277", "title": "Target conversation extraction: Source separation using turn-taking\n  dynamics", "abstract": "  Extracting the speech of participants in a conversation amidst interfering\nspeakers and noise presents a challenging problem. In this paper, we introduce\nthe novel task of target conversation extraction, where the goal is to extract\nthe audio of a target conversation based on the speaker embedding of one of its\nparticipants. To accomplish this, we propose leveraging temporal patterns\ninherent in human conversations, particularly turn-taking dynamics, which\nuniquely characterize speakers engaged in conversation and distinguish them\nfrom interfering speakers and noise. Using neural networks, we show the\nfeasibility of our approach on English and Mandarin conversation datasets. In\nthe presence of interfering speakers, our results show an 8.19 dB improvement\nin signal-to-noise ratio for 2-speaker conversations and a 7.92 dB improvement\nfor 2-4-speaker conversations. Code, dataset available at\nhttps://github.com/chentuochao/Target-Conversation-Extraction.\n", "date": "2024-07-15", "categories": []}
{"id": "2409.15283", "title": "Equivariance-based self-supervised learning for audio signal recovery\n  from clipped measurements", "abstract": "  In numerous inverse problems, state-of-the-art solving strategies involve\ntraining neural networks from ground truth and associated measurement datasets\nthat, however, may be expensive or impossible to collect. Recently,\nself-supervised learning techniques have emerged, with the major advantage of\nno longer requiring ground truth data. Most theoretical and experimental\nresults on self-supervised learning focus on linear inverse problems. The\npresent work aims to study self-supervised learning for the non-linear inverse\nproblem of recovering audio signals from clipped measurements. An\nequivariance-based selfsupervised loss is proposed and studied. Performance is\nassessed on simulated clipped measurements with controlled and varied levels of\nclipping, and further reported on standard real music signals. We show that the\nperformance of the proposed equivariance-based self-supervised declipping\nstrategy compares favorably to fully supervised learning while only requiring\nclipped measurements alone for training.\n", "date": "2024-09-03", "categories": []}
{"id": "2409.15957", "title": "ASD-Diffusion: Anomalous Sound Detection with Diffusion Models", "abstract": "  Unsupervised Anomalous Sound Detection (ASD) aims to design a generalizable\nmethod that can be used to detect anomalies when only normal sounds are given.\nIn this paper, Anomalous Sound Detection based on Diffusion Models\n(ASD-Diffusion) is proposed for ASD in real-world factories. In our pipeline,\nthe anomalies in acoustic features are reconstructed from their noisy corrupted\nfeatures into their approximate normal pattern. Secondly, a post-processing\nanomalies filter algorithm is proposed to detect anomalies that exhibit\nsignificant deviation from the original input after reconstruction.\nFurthermore, denoising diffusion implicit model is introduced to accelerate the\ninference speed by a longer sampling interval of the denoising process. The\nproposed method is innovative in the application of diffusion models as a new\nscheme. Experimental results on the development set of DCASE 2023 challenge\ntask 2 outperform the baseline by 7.75%, demonstrating the effectiveness of the\nproposed method.\n", "date": "2024-09-24", "categories": []}
{"id": "2409.14971", "title": "Blind Spatial Impulse Response Generation from Separate Room- and\n  Scene-Specific Information", "abstract": "  For audio in augmented reality (AR), knowledge of the users' real acoustic\nenvironment is crucial for rendering virtual sounds that seamlessly blend into\nthe environment. As acoustic measurements are usually not feasible in practical\nAR applications, information about the room needs to be inferred from available\nsound sources. Then, additional sound sources can be rendered with the same\nroom acoustic qualities. Crucially, these are placed at different positions\nthan the sources available for estimation. Here, we propose to use an encoder\nnetwork trained using a contrastive loss that maps input sounds to a\nlow-dimensional feature space representing only room-specific information.\nThen, a diffusion-based spatial room impulse response generator is trained to\ntake the latent space and generate a new response, given a new source-receiver\nposition. We show how both room- and position-specific parameters are\nconsidered in the final output.\n", "date": "2024-09-23", "categories": []}
{"id": "2409.12415", "title": "Multichannel-to-Multichannel Target Sound Extraction Using Direction and\n  Timestamp Clues", "abstract": "  We propose a multichannel-to-multichannel target sound extraction (M2M-TSE)\nframework for separating multichannel target signals from a multichannel\nmixture of sound sources. Target sound extraction (TSE) isolates a specific\ntarget signal using user-provided clues, typically focusing on single-channel\nextraction with class labels or temporal activation maps. However, to preserve\nand utilize spatial information in multichannel audio signals, it is essential\nto extract multichannel signals of a target sound source. Moreover, the clue\nfor extraction can also include spatial or temporal cues like\ndirection-of-arrival (DoA) or timestamps of source activation. To address these\nchallenges, we present an M2M framework that extracts a multichannel sound\nsignal based on spatio-temporal clues. We demonstrate that our\ntransformer-based architecture can successively accomplish the M2M-TSE task for\nmultichannel signals synthesized from audio signals of diverse classes in\ndifferent room environments. Furthermore, we show that the multichannel\nextraction task introduces sufficient inductive bias in the DNN, allowing it to\ndirectly handle DoA clues without utilizing hand-crafted spatial features.\n", "date": "2024-09-19", "categories": []}
{"id": "2409.11964", "title": "Data Efficient Acoustic Scene Classification using Teacher-Informed\n  Confusing Class Instruction", "abstract": "  In this technical report, we describe the SNTL-NTU team's submission for Task\n1 Data-Efficient Low-Complexity Acoustic Scene Classification of the detection\nand classification of acoustic scenes and events (DCASE) 2024 challenge. Three\nsystems are introduced to tackle training splits of different sizes. For small\ntraining splits, we explored reducing the complexity of the provided baseline\nmodel by reducing the number of base channels. We introduce data augmentation\nin the form of mixup to increase the diversity of training samples. For the\nlarger training splits, we use FocusNet to provide confusing class information\nto an ensemble of multiple Patchout faSt Spectrogram Transformer (PaSST) models\nand baseline models trained on the original sampling rate of 44.1 kHz. We use\nKnowledge Distillation to distill the ensemble model to the baseline student\nmodel. Training the systems on the TAU Urban Acoustic Scene 2022 Mobile\ndevelopment dataset yielded the highest average testing accuracy of (62.21,\n59.82, 56.81, 53.03, 47.97)% on split (100, 50, 25, 10, 5)% respectively over\nthe three systems.\n", "date": "2024-09-18", "categories": []}
{"id": "2409.02489", "title": "NeuroSpex: Neuro-Guided Speaker Extraction with Cross-Modal Attention", "abstract": "  In the study of auditory attention, it has been revealed that there exists a\nrobust correlation between attended speech and elicited neural responses,\nmeasurable through electroencephalography (EEG). Therefore, it is possible to\nuse the attention information available within EEG signals to guide the\nextraction of the target speaker in a cocktail party computationally. In this\npaper, we present a neuro-guided speaker extraction model, i.e. NeuroSpex,\nusing the EEG response of the listener as the sole auxiliary reference cue to\nextract attended speech from monaural speech mixtures. We propose a novel EEG\nsignal encoder that captures the attention information. Additionally, we\npropose a cross-attention (CA) mechanism to enhance the speech feature\nrepresentations, generating a speaker extraction mask. Experimental results on\na publicly available dataset demonstrate that our proposed model outperforms\ntwo baseline models across various evaluation metrics.\n", "date": "2024-09-04", "categories": []}
{"id": "2409.08997", "title": "Biomimetic Frontend for Differentiable Audio Processing", "abstract": "  While models in audio and speech processing are becoming deeper and more\nend-to-end, they as a consequence need expensive training on large data, and\nare often brittle. We build on a classical model of human hearing and make it\ndifferentiable, so that we can combine traditional explainable biomimetic\nsignal processing approaches with deep-learning frameworks. This allows us to\narrive at an expressive and explainable model that is easily trained on modest\namounts of data. We apply this model to audio processing tasks, including\nclassification and enhancement. Results show that our differentiable model\nsurpasses black-box approaches in terms of computational efficiency and\nrobustness, even with little training data. We also discuss other potential\napplications.\n", "date": "2024-09-13", "categories": []}
{"id": "2303.05896", "title": "Distribution Preserving Source Separation With Time Frequency Predictive\n  Models", "abstract": "  We provide an example of a distribution preserving source separation method,\nwhich aims at addressing perceptual shortcomings of state-of-the-art methods.\nOur approach uses unconditioned generative models of signal sources.\nReconstruction is achieved by means of mix-consistent sampling from a\ndistribution conditioned on a realization of a mix. The separated signals\nfollow their respective source distributions, which provides an advantage when\nseparation results are evaluated in a listening test.\n", "date": "2023-03-10", "categories": []}
{"id": "2409.07858", "title": "Audio Decoding by Inverse Problem Solving", "abstract": "  We consider audio decoding as an inverse problem and solve it through\ndiffusion posterior sampling. Explicit conditioning functions are developed for\ninput signal measurements provided by an example of a transform domain\nperceptual audio codec. Viability is demonstrated by evaluating arbitrary\npairings of a set of bitrates and task-agnostic prior models. For instance, we\nobserve significant improvements on piano while maintaining speech performance\nwhen a speech model is replaced by a joint model trained on both speech and\npiano. With a more general music model, improved decoding compared to legacy\nmethods is obtained for a broad range of content types and bitrates. The noisy\nmean model, underlying the proposed derivation of conditioning, enables a\nsignificant reduction of gradient evaluations for diffusion posterior sampling,\ncompared to methods based on Tweedie's mean. Combining Tweedie's mean with our\nconditioning functions improves the objective performance. An audio demo is\navailable at https://dpscodec-demo.github.io/.\n", "date": "2024-09-12", "categories": []}
{"id": "2409.06196", "title": "MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregating for\n  Heterogeneous Sound Event Detection", "abstract": "  Sound Event Detection (SED) plays a vital role in comprehending and\nperceiving acoustic scenes. Previous methods have demonstrated impressive\ncapabilities. However, they are deficient in learning features of complex\nscenes from heterogeneous dataset. In this paper, we introduce a novel\ndual-branch architecture named Mutual-Assistance Tuning and Dual-Branch\nAggregating for Heterogeneous Sound Event Detection (MTDA-HSED). The MTDA-HSED\narchitecture employs the Mutual-Assistance Audio Adapter (M3A) to effectively\ntackle the multi-scenario problem and uses the Dual-Branch Mid-Fusion (DBMF)\nmodule to tackle the multi-granularity problem. Specifically, M3A is integrated\ninto the BEATs block as an adapter to improve the BEATs' performance by\nfine-tuning it on the multi-scenario dataset. The DBMF module connects BEATs\nand CNN branches, which facilitates the deep fusion of information from the\nBEATs and the CNN branches. Experimental results show that the proposed methods\nexceed the baseline of mpAUC by \\textbf{$5\\%$} on the DESED and MAESTRO Real\ndatasets. Code is available at https://github.com/Visitor-W/MTDA.\n", "date": "2024-09-10", "categories": []}
{"id": "2404.02063", "title": "SPMamba: State-space model is all you need in speech separation", "abstract": "  Existing CNN-based speech separation models face local receptive field\nlimitations and cannot effectively capture long time dependencies. Although\nLSTM and Transformer-based speech separation models can avoid this problem,\ntheir high complexity makes them face the challenge of computational resources\nand inference efficiency when dealing with long audio. To address this\nchallenge, we introduce an innovative speech separation method called SPMamba.\nThis model builds upon the robust TF-GridNet architecture, replacing its\ntraditional BLSTM modules with bidirectional Mamba modules. These modules\neffectively model the spatiotemporal relationships between the time and\nfrequency dimensions, allowing SPMamba to capture long-range dependencies with\nlinear computational complexity. Specifically, the bidirectional processing\nwithin the Mamba modules enables the model to utilize both past and future\ncontextual information, thereby enhancing separation performance. Extensive\nexperiments conducted on public datasets, including WSJ0-2Mix, WHAM!, and\nLibri2Mix, as well as the newly constructed Echo2Mix dataset, demonstrated that\nSPMamba significantly outperformed existing state-of-the-art models, achieving\nsuperior results while also reducing computational complexity. These findings\nhighlighted the effectiveness of SPMamba in tackling the intricate challenges\nof speech separation in complex environments.\n", "date": "2024-04-02", "categories": []}
{"id": "2209.03275", "title": "Multimodal Speech Enhancement Using Burst Propagation", "abstract": "  This paper proposes the MBURST, a novel multimodal solution for audio-visual\nspeech enhancements that consider the most recent neurological discoveries\nregarding pyramidal cells of the prefrontal cortex and other brain regions. The\nso-called burst propagation implements several criteria to address the credit\nassignment problem in a more biologically plausible manner: steering the sign\nand magnitude of plasticity through feedback, multiplexing the feedback and\nfeedforward information across layers through different weight connections,\napproximating feedback and feedforward connections, and linearizing the\nfeedback signals. MBURST benefits from such capabilities to learn correlations\nbetween the noisy signal and the visual stimuli, thus attributing meaning to\nthe speech by amplifying relevant information and suppressing noise.\nExperiments conducted over a Grid Corpus and CHiME3-based dataset show that\nMBURST can reproduce similar mask reconstructions to the multimodal\nbackpropagation-based baseline while demonstrating outstanding energy\nefficiency management, reducing the neuron firing rates to values up to\n\\textbf{$70\\%$} lower. Such a feature implies more sustainable implementations,\nsuitable and desirable for hearing aids or any other similar embedded systems.\n", "date": "2022-09-07", "categories": []}
{"id": "2312.02683", "title": "Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions\n  Using a Heun-Based Sampler", "abstract": "  Diffusion models are a new class of generative models that have recently been\napplied to speech enhancement successfully. Previous works have demonstrated\ntheir superior performance in mismatched conditions compared to state-of-the\nart discriminative models. However, this was investigated with a single\ndatabase for training and another one for testing, which makes the results\nhighly dependent on the particular databases. Moreover, recent developments\nfrom the image generation literature remain largely unexplored for speech\nenhancement. These include several design aspects of diffusion models, such as\nthe noise schedule or the reverse sampler. In this work, we systematically\nassess the generalization performance of a diffusion-based speech enhancement\nmodel by using multiple speech, noise and binaural room impulse response (BRIR)\ndatabases to simulate mismatched acoustic conditions. We also experiment with a\nnoise schedule and a sampler that have not been applied to speech enhancement\nbefore. We show that the proposed system substantially benefits from using\nmultiple databases for training, and achieves superior performance compared to\nstate-of-the-art discriminative models in both matched and mismatched\nconditions. We also show that a Heun-based sampler achieves superior\nperformance at a smaller computational cost compared to a sampler commonly used\nfor speech enhancement.\n", "date": "2023-12-05", "categories": []}
{"id": "2409.04949", "title": "Attention-Based Efficient Breath Sound Removal in Studio Audio\n  Recordings", "abstract": "  In this research, we present an innovative, parameter-efficient model that\nutilizes the attention U-Net architecture for the automatic detection and\neradication of non-speech vocal sounds, specifically breath sounds, in vocal\nrecordings. This task is of paramount importance in the field of sound\nengineering, despite being relatively under-explored. The conventional manual\nprocess for detecting and eliminating these sounds requires significant\nexpertise and is extremely time-intensive. Existing automated detection and\nremoval methods often fall short in terms of efficiency and precision. Our\nproposed model addresses these limitations by offering a streamlined process\nand superior accuracy, achieved through the application of advanced deep\nlearning techniques. A unique dataset, derived from Device and Produced Speech\n(DAPS), was employed for this purpose. The training phase of the model\nemphasizes a log spectrogram and integrates an early stopping mechanism to\nprevent overfitting. Our model not only conserves precious time for sound\nengineers but also enhances the quality and consistency of audio production.\nThis constitutes a significant breakthrough, as evidenced by its comparative\nefficiency, necessitating only 1.9M parameters and a training duration of 3.2\nhours - markedly less than the top-performing models in this domain. The model\nis capable of generating identical outputs as previous models with drastically\nimproved precision, making it an optimal choice.\n", "date": "2024-09-08", "categories": []}
{"id": "2409.04953", "title": "Evaluating Neural Networks Architectures for Spring Reverb Modelling", "abstract": "  Reverberation is a key element in spatial audio perception, historically\nachieved with the use of analogue devices, such as plate and spring reverb, and\nin the last decades with digital signal processing techniques that have allowed\ndifferent approaches for Virtual Analogue Modelling (VAM). The\nelectromechanical functioning of the spring reverb makes it a nonlinear system\nthat is difficult to fully emulate in the digital domain with white-box\nmodelling techniques. In this study, we compare five different neural network\narchitectures, including convolutional and recurrent models, to assess their\neffectiveness in replicating the characteristics of this audio effect. The\nevaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz.\nThis paper specifically focuses on neural audio architectures that offer\nparametric control, aiming to advance the boundaries of current black-box\nmodelling techniques in the domain of spring reverberation.\n", "date": "2024-09-08", "categories": []}
{"id": "2409.05095", "title": "The first Cadenza challenges: using machine learning competitions to\n  improve music for listeners with a hearing loss", "abstract": "  It is well established that listening to music is an issue for those with\nhearing loss, and hearing aids are not a universal solution. How can machine\nlearning be used to address this? This paper details the first application of\nthe open challenge methodology to use machine learning to improve audio quality\nof music for those with hearing loss. The first challenge was a stand-alone\ncompetition (CAD1) and had 9 entrants. The second was an 2024 ICASSP grand\nchallenge (ICASSP24) and attracted 17 entrants. The challenge tasks concerned\ndemixing and remixing pop/rock music to allow a personalised rebalancing of the\ninstruments in the mix, along with amplification to correct for raised hearing\nthresholds. The software baselines provided for entrants to build upon used two\nstate-of-the-art demix algorithms: Hybrid Demucs and Open-Unmix. Evaluation of\nsystems was done using the objective metric HAAQI, the Hearing-Aid Audio\nQuality Index. No entrants improved on the best baseline in CAD1 because there\nwas insufficient room for improvement. Consequently, for ICASSP24 the scenario\nwas made more difficult by using loudspeaker reproduction and specified gains\nto be applied before remixing. This also made the scenario more useful for\nlistening through hearing aids. 9 entrants scored better than the the best\nICASSP24 baseline. Most entrants used a refined version of Hybrid Demucs and\nNAL-R amplification. The highest scoring system combined the outputs of several\ndemixing algorithms in an ensemble approach. These challenges are now open\nbenchmarks for future research with the software and data being freely\navailable.\n", "date": "2024-09-08", "categories": []}
{"id": "2409.00819", "title": "LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant\n  Multi-Talker Speech Separation, ASR and Speaker Diarization", "abstract": "  The evolving speech processing landscape is increasingly focused on complex\nscenarios like meetings or cocktail parties with multiple simultaneous speakers\nand far-field conditions. Existing methodologies for addressing these\nchallenges fall into two categories: multi-channel and single-channel\nsolutions. Single-channel approaches, notable for their generality and\nconvenience, do not require specific information about microphone arrays.\n  This paper presents a large-scale far-field overlapping speech dataset,\ncrafted to advance research in speech separation, recognition, and speaker\ndiarization. This dataset is a critical resource for decoding ``Who said What\nand When'' in multi-talker, reverberant environments, a daunting challenge in\nthe field. Additionally, we introduce a pipeline system encompassing speech\nseparation, recognition, and diarization as a foundational benchmark.\nEvaluations on the WHAMR! dataset validate the broad applicability of the\nproposed data.\n", "date": "2024-09-01", "categories": []}
{"id": "2409.01352", "title": "Spectron: Target Speaker Extraction using Conditional Transformer with\n  Adversarial Refinement", "abstract": "  Recently, attention-based transformers have become a de facto standard in\nmany deep learning applications including natural language processing, computer\nvision, signal processing, etc.. In this paper, we propose a transformer-based\nend-to-end model to extract a target speaker's speech from a monaural\nmulti-speaker mixed audio signal. Unlike existing speaker extraction methods,\nwe introduce two additional objectives to impose speaker embedding consistency\nand waveform encoder invertibility and jointly train both speaker encoder and\nspeech separator to better capture the speaker conditional embedding.\nFurthermore, we leverage a multi-scale discriminator to refine the perceptual\nquality of the extracted speech. Our experiments show that the use of a dual\npath transformer in the separator backbone along with proposed training\nparadigm improves the CNN baseline by $3.12$ dB points. Finally, we compare our\napproach with recent state-of-the-arts and show that our model outperforms\nexisting methods by $4.1$ dB points on an average without creating additional\ndata dependency.\n", "date": "2024-09-02", "categories": []}
{"id": "2408.17166", "title": "Learning Multi-Target TDOA Features for Sound Event Localization and\n  Detection", "abstract": "  Sound event localization and detection (SELD) systems using audio recordings\nfrom a microphone array rely on spatial cues for determining the location of\nsound events. As a consequence, the localization performance of such systems is\nto a large extent determined by the quality of the audio features that are used\nas inputs to the system. We propose a new feature, based on neural generalized\ncross-correlations with phase-transform (NGCC-PHAT), that learns audio\nrepresentations suitable for localization. Using permutation invariant training\nfor the time-difference of arrival (TDOA) estimation problem enables NGCC-PHAT\nto learn TDOA features for multiple overlapping sound events. These features\ncan be used as a drop-in replacement for GCC-PHAT inputs to a SELD-network. We\ntest our method on the STARSS23 dataset and demonstrate improved localization\nperformance compared to using standard GCC-PHAT or SALSA-Lite input features.\n", "date": "2024-08-30", "categories": []}
{"id": "2408.16126", "title": "Improving Generalization of Speech Separation in Real-World Scenarios:\n  Strategies in Simulation, Optimization, and Evaluation", "abstract": "  Achieving robust speech separation for overlapping speakers in various\nacoustic environments with noise and reverberation remains an open challenge.\nAlthough existing datasets are available to train separators for specific\nscenarios, they do not effectively generalize across diverse real-world\nscenarios. In this paper, we present a novel data simulation pipeline that\nproduces diverse training data from a range of acoustic environments and\ncontent, and propose new training paradigms to improve quality of a general\nspeech separation model. Specifically, we first introduce AC-SIM, a data\nsimulation pipeline that incorporates broad variations in both content and\nacoustics. Then we integrate multiple training objectives into the permutation\ninvariant training (PIT) to enhance separation quality and generalization of\nthe trained model. Finally, we conduct comprehensive objective and human\nlistening experiments across separation architectures and benchmarks to\nvalidate our methods, demonstrating substantial improvement of generalization\non both non-homologous and real-world test sets.\n", "date": "2024-08-28", "categories": []}
{"id": "2309.02539", "title": "A Generalized Bandsplit Neural Network for Cinematic Audio Source\n  Separation", "abstract": "  Cinematic audio source separation is a relatively new subtask of audio source\nseparation, with the aim of extracting the dialogue, music, and effects stems\nfrom their mixture. In this work, we developed a model generalizing the\nBandsplit RNN for any complete or overcomplete partitions of the frequency\naxis. Psychoacoustically motivated frequency scales were used to inform the\nband definitions which are now defined with redundancy for more reliable\nfeature extraction. A loss function motivated by the signal-to-noise ratio and\nthe sparsity-promoting property of the 1-norm was proposed. We additionally\nexploit the information-sharing property of a common-encoder setup to reduce\ncomputational complexity during both training and inference, improve separation\nperformance for hard-to-generalize classes of sounds, and allow flexibility\nduring inference time with detachable decoders. Our best model sets the state\nof the art on the Divide and Remaster dataset with performance above the ideal\nratio mask for the dialogue stem.\n", "date": "2023-09-05", "categories": []}
{"id": "2312.10305", "title": "Self-Supervised Disentangled Representation Learning for Robust Target\n  Speech Extraction", "abstract": "  Speech signals are inherently complex as they encompass both global acoustic\ncharacteristics and local semantic information. However, in the task of target\nspeech extraction, certain elements of global and local semantic information in\nthe reference speech, which are irrelevant to speaker identity, can lead to\nspeaker confusion within the speech extraction network. To overcome this\nchallenge, we propose a self-supervised disentangled representation learning\nmethod. Our approach tackles this issue through a two-phase process, utilizing\na reference speech encoding network and a global information disentanglement\nnetwork to gradually disentangle the speaker identity information from other\nirrelevant factors. We exclusively employ the disentangled speaker identity\ninformation to guide the speech extraction network. Moreover, we introduce the\nadaptive modulation Transformer to ensure that the acoustic representation of\nthe mixed signal remains undisturbed by the speaker embeddings. This component\nincorporates speaker embeddings as conditional information, facilitating\nnatural and efficient guidance for the speech extraction network. Experimental\nresults substantiate the effectiveness of our meticulously crafted approach,\nshowcasing a substantial reduction in the likelihood of speaker confusion.\n", "date": "2023-12-16", "categories": []}
{"id": "2406.18747", "title": "A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond\n  Four Stems", "abstract": "  Despite significant recent progress across multiple subtasks of audio source\nseparation, few music source separation systems support separation beyond the\nfour-stem vocals, drums, bass, and other (VDBO) setup. Of the very few current\nsystems that support source separation beyond this setup, most continue to rely\non an inflexible decoder setup that can only support a fixed pre-defined set of\nstems. Increasing stem support in these inflexible systems correspondingly\nrequires increasing computational complexity, rendering extensions of these\nsystems computationally infeasible for long-tail instruments. In this work, we\npropose Banquet, a system that allows source separation of multiple stems using\njust one decoder. A bandsplit source separation model is extended to work in a\nquery-based setup in tandem with a music instrument recognition PaSST model. On\nthe MoisesDB dataset, Banquet, at only 24.9 M trainable parameters, approached\nthe performance level of the significantly more complex 6-stem Hybrid\nTransformer Demucs on VDBO stems and outperformed it on guitar and piano. The\nquery-based setup allows for the separation of narrow instrument classes such\nas clean acoustic guitars, and can be successfully applied to the extraction of\nless common stems such as reeds and organs. Implementation is available at\nhttps://github.com/kwatcharasupat/query-bandit.\n", "date": "2024-06-26", "categories": []}
{"id": "2407.07275", "title": "Remastering Divide and Remaster: A Cinematic Audio Source Separation\n  Dataset with Multilingual Support", "abstract": "  Cinematic audio source separation (CASS), as a problem of extracting the\ndialogue, music, and effects stems from their mixture, is a relatively new\nsubtask of audio source separation. To date, only one publicly available\ndataset exists for CASS, that is, the Divide and Remaster (DnR) dataset, which\nis currently at version 2. While DnR v2 has been an incredibly useful resource\nfor CASS, several areas of improvement have been identified, particularly\nthrough its use in the 2023 Sound Demixing Challenge. In this work, we develop\nversion 3 of the DnR dataset, addressing issues relating to vocal content in\nnon-dialogue stems, loudness distributions, mastering process, and linguistic\ndiversity. In particular, the dialogue stem of DnR v3 includes speech content\nfrom more than 30 languages from multiple families including but not limited to\nthe Germanic, Romance, Indo-Aryan, Dravidian, Malayo-Polynesian, and Bantu\nfamilies. Benchmark results using the Bandit model indicated that training on\nmultilingual data yields significant generalizability to the model even in\nlanguages with low data availability. Even in languages with high data\navailability, the multilingual model often performs on par or better than\ndedicated models trained on monolingual CASS datasets. Dataset and model\nimplementation will be made available at\nhttps://github.com/kwatcharasupat/source-separation-landing.\n", "date": "2024-07-09", "categories": []}
{"id": "2408.03588", "title": "Facing the Music: Tackling Singing Voice Separation in Cinematic Audio\n  Source Separation", "abstract": "  Cinematic audio source separation (CASS), as a standalone problem of\nextracting individual stems from their mixture, is a fairly new subtask of\naudio source separation. A typical setup of CASS is a three-stem problem, with\nthe aim of separating the mixture into the dialogue (DX), music (MX), and\neffects (FX) stems. Given the creative nature of cinematic sound production,\nhowever, several edge cases exist; some sound sources do not fit neatly in any\nof these three stems, necessitating the use of additional auxiliary stems in\nproduction. One very common edge case is the singing voice in film audio, which\nmay belong in either the DX or MX or neither, depending heavily on the\ncinematic context. In this work, we demonstrate a very straightforward\nextension of the dedicated-decoder Bandit and query-based single-decoder\nBanquet models to a four-stem problem, treating non-musical dialogue,\ninstrumental music, singing voice, and effects as separate stems.\nInterestingly, the query-based Banquet model outperformed the dedicated-decoder\nBandit model. We hypothesized that this is due to a better feature alignment at\nthe bottleneck as enforced by the band-agnostic FiLM layer. Dataset and model\nimplementation will be made available at\nhttps://github.com/kwatcharasupat/source-separation-landing.\n", "date": "2024-08-07", "categories": []}
{"id": "2408.10807", "title": "DisMix: Disentangling Mixtures of Musical Instruments for Source-level\n  Pitch and Timbre Manipulation", "abstract": "  Existing work on pitch and timbre disentanglement has been mostly focused on\nsingle-instrument music audio, excluding the cases where multiple instruments\nare presented. To fill the gap, we propose DisMix, a generative framework in\nwhich the pitch and timbre representations act as modular building blocks for\nconstructing the melody and instrument of a source, and the collection of which\nforms a set of per-instrument latent representations underlying the observed\nmixture. By manipulating the representations, our model samples mixtures with\nnovel combinations of pitch and timbre of the constituent instruments. We can\njointly learn the disentangled pitch-timbre representations and a latent\ndiffusion transformer that reconstructs the mixture conditioned on the set of\nsource-level representations. We evaluate the model using both a simple dataset\nof isolated chords and a realistic four-part chorales in the style of J.S.\nBach, identify the key components for the success of disentanglement, and\ndemonstrate the application of mixture transformation based on source-level\nattribute manipulation.\n", "date": "2024-08-20", "categories": []}
{"id": "2408.09792", "title": "Unsupervised Composable Representations for Audio", "abstract": "  Current generative models are able to generate high-quality artefacts but\nhave been shown to struggle with compositional reasoning, which can be defined\nas the ability to generate complex structures from simpler elements. In this\npaper, we focus on the problem of compositional representation learning for\nmusic data, specifically targeting the fully-unsupervised setting. We propose a\nsimple and extensible framework that leverages an explicit compositional\ninductive bias, defined by a flexible auto-encoding objective that can leverage\nany of the current state-of-art generative models. We demonstrate that our\nframework, used with diffusion models, naturally addresses the task of\nunsupervised audio source separation, showing that our model is able to perform\nhigh-quality separation. Our findings reveal that our proposal achieves\ncomparable or superior performance with respect to other blind source\nseparation methods and, furthermore, it even surpasses current state-of-art\nsupervised baselines on signal-to-interference ratio metrics. Additionally, by\nlearning an a-posteriori masking diffusion model in the space of composable\nrepresentations, we achieve a system capable of seamlessly performing\nunsupervised source separation, unconditional generation, and variation\ngeneration. Finally, as our proposal works in the latent space of pre-trained\nneural audio codecs, it also provides a lower computational cost with respect\nto other neural baselines.\n", "date": "2024-08-19", "categories": []}
{"id": "2408.08729", "title": "ConcateNet: Dialogue Separation Using Local And Global Feature\n  Concatenation", "abstract": "  Dialogue separation involves isolating a dialogue signal from a mixture, such\nas a movie or a TV program. This can be a necessary step to enable dialogue\nenhancement for broadcast-related applications. In this paper, ConcateNet for\ndialogue separation is proposed, which is based on a novel approach for\nprocessing local and global features aimed at better generalization for\nout-of-domain signals. ConcateNet is trained using a noise reduction-focused,\npublicly available dataset and evaluated using three datasets: two noise\nreduction-focused datasets (in-domain), which show competitive performance for\nConcateNet, and a broadcast-focused dataset (out-of-domain), which verifies the\nbetter generalization performance for the proposed architecture compared to\nconsidered state-of-the-art noise-reduction methods.\n", "date": "2024-08-16", "categories": []}
{"id": "2408.07547", "title": "PeriodWave: Multi-Period Flow Matching for High-Fidelity Waveform\n  Generation", "abstract": "  Recently, universal waveform generation tasks have been investigated\nconditioned on various out-of-distribution scenarios. Although GAN-based\nmethods have shown their strength in fast waveform generation, they are\nvulnerable to train-inference mismatch scenarios such as two-stage\ntext-to-speech. Meanwhile, diffusion-based models have shown their powerful\ngenerative performance in other domains; however, they stay out of the\nlimelight due to slow inference speed in waveform generation tasks. Above all,\nthere is no generator architecture that can explicitly disentangle the natural\nperiodic features of high-resolution waveform signals. In this paper, we\npropose PeriodWave, a novel universal waveform generation model. First, we\nintroduce a period-aware flow matching estimator that can capture the periodic\nfeatures of the waveform signal when estimating the vector fields.\nAdditionally, we utilize a multi-period estimator that avoids overlaps to\ncapture different periodic features of waveform signals. Although increasing\nthe number of periods can improve the performance significantly, this requires\nmore computational costs. To reduce this issue, we also propose a single\nperiod-conditional universal estimator that can feed-forward parallel by\nperiod-wise batch inference. Additionally, we utilize discrete wavelet\ntransform to losslessly disentangle the frequency information of waveform\nsignals for high-frequency modeling, and introduce FreeU to reduce the\nhigh-frequency noise for waveform generation. The experimental results\ndemonstrated that our model outperforms the previous models both in\nMel-spectrogram reconstruction and text-to-speech tasks. All source code will\nbe available at \\url{https://github.com/sh-lee-prml/PeriodWave}.\n", "date": "2024-08-14", "categories": []}
{"id": "2408.06500", "title": "Music2Latent: Consistency Autoencoders for Latent Audio Compression", "abstract": "  Efficient audio representations in a compressed continuous latent space are\ncritical for generative audio modeling and Music Information Retrieval (MIR)\ntasks. However, some existing audio autoencoders have limitations, such as\nmulti-stage training procedures, slow iterative sampling, or low reconstruction\nquality. We introduce Music2Latent, an audio autoencoder that overcomes these\nlimitations by leveraging consistency models. Music2Latent encodes samples into\na compressed continuous latent space in a single end-to-end training process\nwhile enabling high-fidelity single-step reconstruction. Key innovations\ninclude conditioning the consistency model on upsampled encoder outputs at all\nlevels through cross connections, using frequency-wise self-attention to\ncapture long-range frequency dependencies, and employing frequency-wise learned\nscaling to handle varying value distributions across frequencies at different\nnoise levels. We demonstrate that Music2Latent outperforms existing continuous\naudio autoencoders in sound quality and reconstruction accuracy while achieving\ncompetitive performance on downstream MIR tasks using its latent\nrepresentations. To our knowledge, this represents the first successful attempt\nat training an end-to-end consistency autoencoder model.\n", "date": "2024-08-12", "categories": []}
{"id": "2408.07020", "title": "Source Separation of Multi-source Raw Music using a Residual Quantized\n  Variational Autoencoder", "abstract": "  I developed a neural audio codec model based on the residual quantized\nvariational autoencoder architecture. I train the model on the Slakh2100\ndataset, a standard dataset for musical source separation, composed of\nmulti-track audio. The model can separate audio sources, achieving almost SoTA\nresults with much less computing power. The code is publicly available at\ngithub.com/LeonardoBerti00/Source-Separation-of-Multi-source-Music-using-Residual-Quantizad-Variational-Autoencoder\n", "date": "2024-08-12", "categories": []}
{"id": "2408.06264", "title": "Audio Enhancement for Computer Audition -- An Iterative Training\n  Paradigm Using Sample Importance", "abstract": "  Neural network models for audio tasks, such as automatic speech recognition\n(ASR) and acoustic scene classification (ASC), are susceptible to noise\ncontamination for real-life applications. To improve audio quality, an\nenhancement module, which can be developed independently, is explicitly used at\nthe front-end of the target audio applications. In this paper, we present an\nend-to-end learning solution to jointly optimise the models for audio\nenhancement (AE) and the subsequent applications. To guide the optimisation of\nthe AE module towards a target application, and especially to overcome\ndifficult samples, we make use of the sample-wise performance measure as an\nindication of sample importance. In experiments, we consider four\nrepresentative applications to evaluate our training paradigm, i.e., ASR,\nspeech command recognition (SCR), speech emotion recognition (SER), and ASC.\nThese applications are associated with speech and non-speech tasks concerning\nsemantic and non-semantic features, transient and global information, and the\nexperimental results indicate that our proposed approach can considerably boost\nthe noise robustness of the models, especially at low signal-to-noise ratios\n(SNRs), for a wide range of computer audition tasks in everyday-life noisy\nenvironments.\n", "date": "2024-08-12", "categories": []}
{"id": "2408.05057", "title": "SELD-Mamba: Selective State-Space Model for Sound Event Localization and\n  Detection with Source Distance Estimation", "abstract": "  In the Sound Event Localization and Detection (SELD) task, Transformer-based\nmodels have demonstrated impressive capabilities. However, the quadratic\ncomplexity of the Transformer's self-attention mechanism results in\ncomputational inefficiencies. In this paper, we propose a network architecture\nfor SELD called SELD-Mamba, which utilizes Mamba, a selective state-space\nmodel. We adopt the Event-Independent Network V2 (EINV2) as the foundational\nframework and replace its Conformer blocks with bidirectional Mamba blocks to\ncapture a broader range of contextual information while maintaining\ncomputational efficiency. Additionally, we implement a two-stage training\nmethod, with the first stage focusing on Sound Event Detection (SED) and\nDirection of Arrival (DoA) estimation losses, and the second stage\nreintroducing the Source Distance Estimation (SDE) loss. Our experimental\nresults on the 2024 DCASE Challenge Task3 dataset demonstrate the effectiveness\nof the selective state-space model in SELD and highlight the benefits of the\ntwo-stage training approach in enhancing SELD performance.\n", "date": "2024-08-09", "categories": []}
{"id": "2207.03927", "title": "BAST: Binaural Audio Spectrogram Transformer for Binaural Sound\n  Localization", "abstract": "  Accurate sound localization in a reverberation environment is essential for\nhuman auditory perception. Recently, Convolutional Neural Networks (CNNs) have\nbeen utilized to model the binaural human auditory pathway. However, CNN shows\nbarriers in capturing the global acoustic features. To address this issue, we\npropose a novel end-to-end Binaural Audio Spectrogram Transformer (BAST) model\nto predict the sound azimuth in both anechoic and reverberation environments.\nTwo modes of implementation, i.e. BAST-SP and BAST-NSP corresponding to BAST\nmodel with shared and non-shared parameters respectively, are explored. Our\nmodel with subtraction interaural integration and hybrid loss achieves an\nangular distance of 1.29 degrees and a Mean Square Error of 1e-3 at all\nazimuths, significantly surpassing CNN based model. The exploratory analysis of\nthe BAST's performance on the left-right hemifields and anechoic and\nreverberation environments shows its generalization ability as well as the\nfeasibility of binaural Transformers in sound localization. Furthermore, the\nanalysis of the attention maps is provided to give additional insights on the\ninterpretation of the localization process in a natural reverberant\nenvironment.\n", "date": "2022-07-08", "categories": []}
{"id": "2407.21658", "title": "Beat this! Accurate beat tracking without DBN postprocessing", "abstract": "  We propose a system for tracking beats and downbeats with two objectives:\ngenerality across a diverse music range, and high accuracy. We achieve\ngenerality by training on multiple datasets -- including solo instrument\nrecordings, pieces with time signature changes, and classical music with high\ntempo variations -- and by removing the commonly used Dynamic Bayesian Network\n(DBN) postprocessing, which introduces constraints on the meter and tempo. For\nhigh accuracy, among other improvements, we develop a loss function tolerant to\nsmall time shifts of annotations, and an architecture alternating convolutions\nwith transformers either over frequency or time. Our system surpasses the\ncurrent state of the art in F1 score despite using no DBN. However, it can\nstill fail, especially for difficult and underrepresented genres, and performs\nworse on continuity metrics, so we publish our model, code, and preprocessed\ndatasets, and invite others to beat this.\n", "date": "2024-07-31", "categories": []}
{"id": "2407.18571", "title": "Speech Bandwidth Expansion Via High Fidelity Generative Adversarial\n  Networks", "abstract": "  Speech bandwidth expansion is crucial for expanding the frequency range of\nlow-bandwidth speech signals, thereby improving audio quality, clarity and\nperceptibility in digital applications. Its applications span telephony,\ncompression, text-to-speech synthesis, and speech recognition. This paper\npresents a novel approach using a high-fidelity generative adversarial network,\nunlike cascaded systems, our system is trained end-to-end on paired narrowband\nand wideband speech signals. Our method integrates various bandwidth upsampling\nratios into a single unified model specifically designed for speech bandwidth\nexpansion applications. Our approach exhibits robust performance across various\nbandwidth expansion factors, including those not encountered during training,\ndemonstrating zero-shot capability. To the best of our knowledge, this is the\nfirst work to showcase this capability. The experimental results demonstrate\nthat our method outperforms previous end-to-end approaches, as well as\ninterpolation and traditional techniques, showcasing its effectiveness in\npractical speech enhancement applications.\n", "date": "2024-07-26", "categories": []}
{"id": "2407.18732", "title": "A Physics-Informed Neural Network-Based Approach for the Spatial\n  Upsampling of Spherical Microphone Arrays", "abstract": "  Spherical microphone arrays are convenient tools for capturing the spatial\ncharacteristics of a sound field. However, achieving superior spatial\nresolution requires arrays with numerous capsules, consequently leading to\nexpensive devices. To address this issue, we present a method for spatially\nupsampling spherical microphone arrays with a limited number of capsules. Our\napproach exploits a physics-informed neural network with Rowdy activation\nfunctions, leveraging physical constraints to provide high-order microphone\narray signals, starting from low-order devices. Results show that, within its\ndomain of application, our approach outperforms a state of the art method based\non signal processing for spherical microphone arrays upsampling.\n", "date": "2024-07-26", "categories": []}
{"id": "2407.16643", "title": "Synthesizer Sound Matching Using Audio Spectrogram Transformers", "abstract": "  Systems for synthesizer sound matching, which automatically set the\nparameters of a synthesizer to emulate an input sound, have the potential to\nmake the process of synthesizer programming faster and easier for novice and\nexperienced musicians alike, whilst also affording new means of interaction\nwith synthesizers. Considering the enormous variety of synthesizers in the\nmarketplace, and the complexity of many of them, general-purpose sound matching\nsystems that function with minimal knowledge or prior assumptions about the\nunderlying synthesis architecture are particularly desirable. With this in\nmind, we introduce a synthesizer sound matching model based on the Audio\nSpectrogram Transformer. We demonstrate the viability of this model by training\non a large synthetic dataset of randomly generated samples from the popular\nMassive synthesizer. We show that this model can reconstruct parameters of\nsamples generated from a set of 16 parameters, highlighting its improved\nfidelity relative to multi-layer perceptron and convolutional neural network\nbaselines. We also provide audio examples demonstrating the out-of-domain model\nperformance in emulating vocal imitations, and sounds from other synthesizers\nand musical instruments.\n", "date": "2024-07-23", "categories": []}
{"id": "2407.16691", "title": "Automatic Equalization for Individual Instrument Tracks Using\n  Convolutional Neural Networks", "abstract": "  We propose a novel approach for the automatic equalization of individual\nmusical instrument tracks. Our method begins by identifying the instrument\npresent within a source recording in order to choose its corresponding ideal\nspectrum as a target. Next, the spectral difference between the recording and\nthe target is calculated, and accordingly, an equalizer matching model is used\nto predict settings for a parametric equalizer. To this end, we build upon a\ndifferentiable parametric equalizer matching neural network, demonstrating\nimprovements relative to previously established state-of-the-art. Unlike past\napproaches, we show how our system naturally allows real-world audio data to be\nleveraged during the training of our matching model, effectively generating\nsuitably produced training targets in an automated manner mirroring conditions\nat inference time. Consequently, we illustrate how fine-tuning our matching\nmodel on such examples considerably improves parametric equalizer matching\nperformance in real-world scenarios, decreasing mean absolute error by 24%\nrelative to methods relying solely on random parameter sampling techniques as a\nself-supervised learning strategy. We perform listening tests, and demonstrate\nthat our proposed automatic equalization solution subjectively enhances the\ntonal characteristics for recordings of common instrument types.\n", "date": "2024-07-23", "categories": []}
{"id": "2312.07059", "title": "LSTM-CNN Network for Audio Signature Analysis in Noisy Environments", "abstract": "  There are multiple applications to automatically count people and specify\ntheir gender at work, exhibitions, malls, sales, and industrial usage. Although\ncurrent speech detection methods are supposed to operate well, in most\nsituations, in addition to genders, the number of current speakers is unknown\nand the classification methods are not suitable due to many possible classes.\nIn this study, we focus on a long-short-term memory convolutional neural\nnetwork (LSTM-CNN) to extract time and / or frequency-dependent features of the\nsound data to estimate the number / gender of simultaneous active speakers at\neach frame in noisy environments. Considering the maximum number of speakers as\n10, we have utilized 19000 audio samples with diverse combinations of males,\nfemales, and background noise in public cities, industrial situations, malls,\nexhibitions, workplaces, and nature for learning purposes. This proof of\nconcept shows promising performance with training/validation MSE values of\nabout 0.019/0.017 in detecting count and gender.\n", "date": "2023-12-12", "categories": []}
{"id": "2407.15749", "title": "Robustness of Speech Separation Models for Similar-pitch Speakers", "abstract": "  Single-channel speech separation is a crucial task for enhancing speech\nrecognition systems in multi-speaker environments. This paper investigates the\nrobustness of state-of-the-art Neural Network models in scenarios where the\npitch differences between speakers are minimal. Building on earlier findings by\nDitter and Gerkmann, which identified a significant performance drop for the\n2018 Chimera++ under similar-pitch conditions, our study extends the analysis\nto more recent and sophisticated Neural Network models. Our experiments reveal\nthat modern models have substantially reduced the performance gap for matched\ntraining and testing conditions. However, a substantial performance gap\npersists under mismatched conditions, with models performing well for large\npitch differences but showing worse performance if the speakers' pitches are\nsimilar. These findings motivate further research into the generalizability of\nspeech separation models to similar-pitch speakers and unseen data.\n", "date": "2024-07-22", "categories": []}
{"id": "2407.13264", "title": "Underwater Acoustic Signal Denoising Algorithms: A Survey of the\n  State-of-the-art", "abstract": "  This paper comprehensively reviews recent advances in underwater acoustic\nsignal denoising, an area critical for improving the reliability and clarity of\nunderwater communication and monitoring systems. Despite significant progress\nin the field, the complex nature of underwater environments poses unique\nchallenges that complicate the denoising process. We begin by outlining the\nfundamental challenges associated with underwater acoustic signal processing,\nincluding signal attenuation, noise variability, and the impact of\nenvironmental factors. The review then systematically categorizes and discusses\nvarious denoising algorithms, such as conventional, decomposition-based, and\nlearning-based techniques, highlighting their applications, advantages, and\nlimitations. Evaluation metrics and experimental datasets are also reviewed.\nThe paper concludes with a list of open questions and recommendations for\nfuture research directions, emphasizing the need for developing more robust\ndenoising techniques that can adapt to the dynamic underwater acoustic\nenvironment.\n", "date": "2024-07-18", "categories": []}
{"id": "2407.09732", "title": "Speech Slytherin: Examining the Performance and Efficiency of Mamba for\n  Speech Separation, Recognition, and Synthesis", "abstract": "  It is too early to conclude that Mamba is a better alternative to\ntransformers for speech before comparing Mamba with transformers in terms of\nboth performance and efficiency in multiple speech-related tasks. To reach this\nconclusion, we propose and evaluate three models for three tasks: Mamba-TasNet\nfor speech separation, ConMamba for speech recognition, and VALL-M for speech\nsynthesis. We compare them with transformers of similar sizes in performance,\nmemory, and speed. Our Mamba or Mamba-transformer hybrid models show comparable\nor higher performance than their transformer counterparts: Sepformer,\nConformer, and VALL-E. They are more efficient than transformers in memory and\nspeed for speech longer than a threshold duration, inversely related to the\nresolution of a speech token. Mamba for separation is the most efficient, and\nMamba for recognition is the least. Further, we show that Mamba is not more\nefficient than transformer for speech shorter than the threshold duration and\nperforms worse in models that require joint modeling of text and speech, such\nas cross or masked attention of two inputs. Therefore, we argue that the\nsuperiority of Mamba or transformer depends on particular problems and models.\nCode available at https://github.com/xi-j/Mamba-TasNet and\nhttps://github.com/xi-j/Mamba-ASR.\n", "date": "2024-07-13", "categories": []}
{"id": "2407.10373", "title": "Mutual Learning for Acoustic Matching and Dereverberation via Visual\n  Scene-driven Diffusion", "abstract": "  Visual acoustic matching (VAM) is pivotal for enhancing the immersive\nexperience, and the task of dereverberation is effective in improving audio\nintelligibility. Existing methods treat each task independently, overlooking\nthe inherent reciprocity between them. Moreover, these methods depend on paired\ntraining data, which is challenging to acquire, impeding the utilization of\nextensive unpaired data. In this paper, we introduce MVSD, a mutual learning\nframework based on diffusion models. MVSD considers the two tasks\nsymmetrically, exploiting the reciprocal relationship to facilitate learning\nfrom inverse tasks and overcome data scarcity. Furthermore, we employ the\ndiffusion model as foundational conditional converters to circumvent the\ntraining instability and over-smoothing drawbacks of conventional GAN\narchitectures. Specifically, MVSD employs two converters: one for VAM called\nreverberator and one for dereverberation called dereverberator. The\ndereverberator judges whether the reverberation audio generated by reverberator\nsounds like being in the conditional visual scenario, and vice versa. By\nforming a closed loop, these two converters can generate informative feedback\nsignals to optimize the inverse tasks, even with easily acquired one-way\nunpaired data. Extensive experiments on two standard benchmarks, i.e.,\nSoundSpaces-Speech and Acoustic AVSpeech, exhibit that our framework can\nimprove the performance of the reverberator and dereverberator and better match\nspecified visual scenarios.\n", "date": "2024-07-15", "categories": []}
{"id": "2307.13821", "title": "Fitting Auditory Filterbanks with Multiresolution Neural Networks", "abstract": "  Waveform-based deep learning faces a dilemma between nonparametric and\nparametric approaches. On one hand, convolutional neural networks (convnets)\nmay approximate any linear time-invariant system; yet, in practice, their\nfrequency responses become more irregular as their receptive fields grow. On\nthe other hand, a parametric model such as LEAF is guaranteed to yield Gabor\nfilters, hence an optimal time-frequency localization; yet, this strong\ninductive bias comes at the detriment of representational capacity. In this\npaper, we aim to overcome this dilemma by introducing a neural audio model,\nnamed multiresolution neural network (MuReNN). The key idea behind MuReNN is to\ntrain separate convolutional operators over the octave subbands of a discrete\nwavelet transform (DWT). Since the scale of DWT atoms grows exponentially\nbetween octaves, the receptive fields of the subsequent learnable convolutions\nin MuReNN are dilated accordingly. For a given real-world dataset, we fit the\nmagnitude response of MuReNN to that of a well-established auditory filterbank:\nGammatone for speech, CQT for music, and third-octave for urban sounds,\nrespectively. This is a form of knowledge distillation (KD), in which the\nfilterbank ''teacher'' is engineered by domain knowledge while the neural\nnetwork ''student'' is optimized from data. We compare MuReNN to the state of\nthe art in terms of goodness of fit after KD on a hold-out set and in terms of\nHeisenberg time-frequency localization. Compared to convnets and Gabor\nconvolutions, we find that MuReNN reaches state-of-the-art performance on all\nthree optimization problems.\n", "date": "2023-07-25", "categories": []}
{"id": "2406.12434", "title": "Towards Audio Codec-based Speech Separation", "abstract": "  Recent improvements in neural audio codec (NAC) models have generated\ninterest in adopting pre-trained codecs for a variety of speech processing\napplications to take advantage of the efficiencies gained from high\ncompression, but these have yet been applied to the speech separation (SS)\ntask. SS can benefit from high compression because the compute required for\ntraditional SS models makes them impractical for many edge computing use cases.\nHowever, SS is a waveform-masking task where compression tends to introduce\ndistortions that severely impact performance. Here we propose a novel task of\nAudio Codec-based SS, where SS is performed within the embedding space of a\nNAC, and propose a new model, Codecformer, to address this task. At inference,\nCodecformer achieves a 52x reduction in MAC while producing separation\nperformance comparable to a cloud deployment of Sepformer. This method charts a\nnew direction for performing efficient SS in practical scenarios.\n", "date": "2024-06-18", "categories": []}
{"id": "2407.03736", "title": "Semantic Grouping Network for Audio Source Separation", "abstract": "  Recently, audio-visual separation approaches have taken advantage of the\nnatural synchronization between the two modalities to boost audio source\nseparation performance. They extracted high-level semantics from visual inputs\nas the guidance to help disentangle sound representation for individual\nsources. Can we directly learn to disentangle the individual semantics from the\nsound itself? The dilemma is that multiple sound sources are mixed together in\nthe original space. To tackle the difficulty, in this paper, we present a novel\nSemantic Grouping Network, termed as SGN, that can directly disentangle sound\nrepresentations and extract high-level semantic information for each source\nfrom input audio mixture. Specifically, SGN aggregates category-wise source\nfeatures through learnable class tokens of sounds. Then, the aggregated\nsemantic features can be used as the guidance to separate the corresponding\naudio sources from the mixture. We conducted extensive experiments on\nmusic-only and universal sound separation benchmarks: MUSIC, FUSS, MUSDB18, and\nVGG-Sound. The results demonstrate that our SGN significantly outperforms\nprevious audio-only methods and audio-visual models without utilizing\nadditional visual cues.\n", "date": "2024-07-04", "categories": []}
{"id": "2407.04547", "title": "Real-time Timbre Remapping with Differentiable DSP", "abstract": "  Timbre is a primary mode of expression in diverse musical contexts. However,\nprevalent audio-driven synthesis methods predominantly rely on pitch and\nloudness envelopes, effectively flattening timbral expression from the input.\nOur approach draws on the concept of timbre analogies and investigates how\ntimbral expression from an input signal can be mapped onto controls for a\nsynthesizer. Leveraging differentiable digital signal processing, our method\nfacilitates direct optimization of synthesizer parameters through a novel\nfeature difference loss. This loss function, designed to learn relative timbral\ndifferences between musical events, prioritizes the subtleties of graded timbre\nmodulations within phrases, allowing for meaningful translations in a timbre\nspace. Using snare drum performances as a case study, where timbral expression\nis central, we demonstrate real-time timbre remapping from acoustic snare drums\nto a differentiable synthesizer modeled after the Roland TR-808.\n", "date": "2024-07-05", "categories": []}
{"id": "2407.00657", "title": "Improving Real-Time Music Accompaniment Separation with MMDenseNet", "abstract": "  Music source separation aims to separate polyphonic music into different\ntypes of sources. Most existing methods focus on enhancing the quality of\nseparated results by using a larger model structure, rendering them unsuitable\nfor deployment on edge devices. Moreover, these methods may produce low-quality\noutput when the input duration is short, making them impractical for real-time\napplications. Therefore, the goal of this paper is to enhance a lightweight\nmodel, MMDenstNet, to strike a balance between separation quality and latency\nfor real-time applications. Different directions of improvement are explored or\nproposed in this paper, including complex ideal ratio mask, self-attention,\nband-merge-split method, and feature look back. Source-to-distortion ratio,\nreal-time factor, and optimal latency are employed to evaluate the performance.\nTo align with our application requirements, the evaluation process in this\npaper focuses on the separation performance of the accompaniment part.\nExperimental results demonstrate that our improvement achieves low real-time\nfactor and optimal latency while maintaining acceptable separation quality.\n", "date": "2024-06-30", "categories": []}
{"id": "2407.00888", "title": "Papez: Resource-Efficient Speech Separation with Auditory Working Memory", "abstract": "  Transformer-based models recently reached state-of-the-art single-channel\nspeech separation accuracy; However, their extreme computational load makes it\ndifficult to deploy them in resource-constrained mobile or IoT devices. We thus\npresent Papez, a lightweight and computation-efficient single-channel speech\nseparation model. Papez is based on three key techniques. We first replace the\ninter-chunk Transformer with small-sized auditory working memory. Second, we\nadaptively prune the input tokens that do not need further processing. Finally,\nwe reduce the number of parameters through the recurrent transformer. Our\nextensive evaluation shows that Papez achieves the best resource and accuracy\ntradeoffs with a large margin. We publicly share our source code at\n\\texttt{https://github.com/snuhcs/Papez}\n", "date": "2024-07-01", "categories": []}
{"id": "2406.13385", "title": "Explainable by-design Audio Segmentation through Non-Negative Matrix\n  Factorization and Probing", "abstract": "  Audio segmentation is a key task for many speech technologies, most of which\nare based on neural networks, usually considered as black boxes, with\nhigh-level performances. However, in many domains, among which health or\nforensics, there is not only a need for good performance but also for\nexplanations about the output decision. Explanations derived directly from\nlatent representations need to satisfy \"good\" properties, such as\ninformativeness, compactness, or modularity, to be interpretable. In this\narticle, we propose an explainable-by-design audio segmentation model based on\nnon-negative matrix factorization (NMF) which is a good candidate for the\ndesign of interpretable representations. This paper shows that our model\nreaches good segmentation performances, and presents deep analyses of the\nlatent representation extracted from the non-negative matrix. The proposed\napproach opens new perspectives toward the evaluation of interpretable\nrepresentations according to \"good\" properties.\n", "date": "2024-06-19", "categories": []}
{"id": "2406.13935", "title": "CONMOD: Controllable Neural Frame-based Modulation Effects", "abstract": "  Deep learning models have seen widespread use in modelling LFO-driven audio\neffects, such as phaser and flanger. Although existing neural architectures\nexhibit high-quality emulation of individual effects, they do not possess the\ncapability to manipulate the output via control parameters. To address this\nissue, we introduce Controllable Neural Frame-based Modulation Effects\n(CONMOD), a single black-box model which emulates various LFO-driven effects in\na frame-wise manner, offering control over LFO frequency and feedback\nparameters. Additionally, the model is capable of learning the continuous\nembedding space of two distinct phaser effects, enabling us to steer between\neffects and achieve creative outputs. Our model outperforms previous work while\npossessing both controllability and universality, presenting opportunities to\nenhance creativity in modern LFO-driven audio effects.\n", "date": "2024-06-20", "categories": []}
{"id": "2303.07538", "title": "HiSSNet: Sound Event Detection and Speaker Identification via\n  Hierarchical Prototypical Networks for Low-Resource Headphones", "abstract": "  Modern noise-cancelling headphones have significantly improved users'\nauditory experiences by removing unwanted background noise, but they can also\nblock out sounds that matter to users. Machine learning (ML) models for sound\nevent detection (SED) and speaker identification (SID) can enable headphones to\nselectively pass through important sounds; however, implementing these models\nfor a user-centric experience presents several unique challenges. First, most\npeople spend limited time customizing their headphones, so the sound detection\nshould work reasonably well out of the box. Second, the models should be able\nto learn over time the specific sounds that are important to users based on\ntheir implicit and explicit interactions. Finally, such models should have a\nsmall memory footprint to run on low-power headphones with limited on-chip\nmemory. In this paper, we propose addressing these challenges using HiSSNet\n(Hierarchical SED and SID Network). HiSSNet is an SEID (SED and SID) model that\nuses a hierarchical prototypical network to detect both general and specific\nsounds of interest and characterize both alarm-like and speech sounds. We show\nthat HiSSNet outperforms an SEID model trained using non-hierarchical\nprototypical networks by 6.9 - 8.6 percent. When compared to state-of-the-art\n(SOTA) models trained specifically for SED or SID alone, HiSSNet achieves\nsimilar or better performance while reducing the memory footprint required to\nsupport multiple capabilities on-device.\n", "date": "2023-03-13", "categories": []}
{"id": "2406.08771", "title": "MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal\n  Domains for Sound Event Localization and Detection", "abstract": "  Sound Event Localization and Detection (SELD) involves detecting and\nlocalizing sound events using multichannel sound recordings. Previously\nproposed Event-Independent Network V2 (EINV2) has achieved outstanding\nperformance on SELD. However, it still faces challenges in effectively\nextracting features across spectral, spatial, and temporal domains. This paper\nproposes a three-stage network structure named Multi-scale Feature Fusion (MFF)\nmodule to fully extract multi-scale features across spectral, spatial, and\ntemporal domains. The MFF module utilizes parallel subnetworks architecture to\ngenerate multi-scale spectral and spatial features. The TF-Convolution Module\nis employed to provide multi-scale temporal features. We incorporated MFF into\nEINV2 and term the proposed method as MFF-EINV2. Experimental results in 2022\nand 2023 DCASE challenge task3 datasets show the effectiveness of our\nMFF-EINV2, which achieves state-of-the-art (SOTA) performance compared to\npublished methods.\n", "date": "2024-06-13", "categories": []}
{"id": "2406.11006", "title": "SPEAR: Receiver-to-Receiver Acoustic Neural Warping Field", "abstract": "  We present SPEAR, a continuous receiver-to-receiver acoustic neural warping\nfield for spatial acoustic effects prediction in an acoustic 3D space with a\nsingle stationary audio source. Unlike traditional source-to-receiver modelling\nmethods that require prior space acoustic properties knowledge to rigorously\nmodel audio propagation from source to receiver, we propose to predict by\nwarping the spatial acoustic effects from one reference receiver position to\nanother target receiver position, so that the warped audio essentially\naccommodates all spatial acoustic effects belonging to the target position.\nSPEAR can be trained in a data much more readily accessible manner, in which we\nsimply ask two robots to independently record spatial audio at different\npositions. We further theoretically prove the universal existence of the\nwarping field if and only if one audio source presents. Three physical\nprinciples are incorporated to guide SPEAR network design, leading to the\nlearned warping field physically meaningful. We demonstrate SPEAR superiority\non both synthetic, photo-realistic and real-world dataset, showing the huge\npotential of SPEAR to various down-stream robotic tasks.\n", "date": "2024-06-16", "categories": []}
{"id": "2406.11619", "title": "AV-CrossNet: an Audiovisual Complex Spectral Mapping Network for Speech\n  Separation By Leveraging Narrow- and Cross-Band Modeling", "abstract": "  Adding visual cues to audio-based speech separation can improve separation\nperformance. This paper introduces AV-CrossNet, an audiovisual (AV) system for\nspeech enhancement, target speaker extraction, and multi-talker speaker\nseparation. AV-CrossNet is extended from the CrossNet architecture, which is a\nrecently proposed network that performs complex spectral mapping for speech\nseparation by leveraging global attention and positional encoding. To\neffectively utilize visual cues, the proposed system incorporates pre-extracted\nvisual embeddings and employs a visual encoder comprising temporal\nconvolutional layers. Audio and visual features are fused in an early fusion\nlayer before feeding to AV-CrossNet blocks. We evaluate AV-CrossNet on multiple\ndatasets, including LRS, VoxCeleb, and COG-MHEAR challenge. Evaluation results\ndemonstrate that AV-CrossNet advances the state-of-the-art performance in all\naudiovisual tasks, even on untrained and mismatched datasets.\n", "date": "2024-06-17", "categories": []}
{"id": "2402.00811", "title": "An Analysis of the Variance of Diffusion-based Speech Enhancement", "abstract": "  Diffusion models proved to be powerful models for generative speech\nenhancement. In recent SGMSE+ approaches, training involves a stochastic\ndifferential equation for the diffusion process, adding both Gaussian and\nenvironmental noise to the clean speech signal gradually. The speech\nenhancement performance varies depending on the choice of the stochastic\ndifferential equation that controls the evolution of the mean and the variance\nalong the diffusion processes when adding environmental and Gaussian noise. In\nthis work, we highlight that the scale of the variance is a dominant parameter\nfor speech enhancement performance and show that it controls the tradeoff\nbetween noise attenuation and speech distortions. More concretely, we show that\na larger variance increases the noise attenuation and allows for reducing the\ncomputational footprint, as fewer function evaluations for generating the\nestimate are required\n", "date": "2024-02-01", "categories": []}
{"id": "2406.08914", "title": "Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and\n  Reverberant Multi-Speaker Automatic Speech Recognition", "abstract": "  One solution to automatic speech recognition (ASR) of overlapping speakers is\nto separate speech and then perform ASR on the separated signals. Commonly, the\nseparator produces artefacts which often degrade ASR performance. Addressing\nthis issue typically requires reference transcriptions to jointly train the\nseparation and ASR networks. This is often not viable for training on\nreal-world in-domain audio where reference transcript information is not always\navailable. This paper proposes a transcription-free method for joint training\nusing only audio signals. The proposed method uses embedding differences of\npre-trained ASR encoders as a loss with a proposed modification to permutation\ninvariant training (PIT) called guided PIT (GPIT). The method achieves a 6.4%\nimprovement in word error rate (WER) measures over a signal-level loss and also\nshows enhancement improvements in perceptual measures such as short-time\nobjective intelligibility (STOI).\n", "date": "2024-06-13", "categories": []}
{"id": "2406.09154", "title": "Diffusion Gaussian Mixture Audio Denoise", "abstract": "  Recent diffusion models have achieved promising performances in\naudio-denoising tasks. The unique property of the reverse process could recover\nclean signals. However, the distribution of real-world noises does not comply\nwith a single Gaussian distribution and is even unknown. The sampling of\nGaussian noise conditions limits its application scenarios. To overcome these\nchallenges, we propose a DiffGMM model, a denoising model based on the\ndiffusion and Gaussian mixture models. We employ the reverse process to\nestimate parameters for the Gaussian mixture model. Given a noisy audio signal,\nwe first apply a 1D-U-Net to extract features and train linear layers to\nestimate parameters for the Gaussian mixture model, and we approximate the real\nnoise distributions. The noisy signal is continuously subtracted from the\nestimated noise to output clean audio signals. Extensive experimental results\ndemonstrate that the proposed DiffGMM model achieves state-of-the-art\nperformance.\n", "date": "2024-06-13", "categories": []}
{"id": "2403.11827", "title": "Sound Event Detection and Localization with Distance Estimation", "abstract": "  Sound Event Detection and Localization (SELD) is a combined task of\nidentifying sound events and their corresponding direction-of-arrival (DOA).\nWhile this task has numerous applications and has been extensively researched\nin recent years, it fails to provide full information about the sound source\nposition. In this paper, we overcome this problem by extending the task to\nSound Event Detection, Localization with Distance Estimation (3D SELD). We\nstudy two ways of integrating distance estimation within the SELD core - a\nmulti-task approach, in which the problem is tackled by a separate model\noutput, and a single-task approach obtained by extending the multi-ACCDOA\nmethod to include distance information. We investigate both methods for the\nAmbisonic and binaural versions of STARSS23: Sony-TAU Realistic Spatial\nSoundscapes 2023. Moreover, our study involves experiments on the loss function\nrelated to the distance estimation part. Our results show that it is possible\nto perform 3D SELD without any degradation of performance in sound event\ndetection and DOA estimation.\n", "date": "2024-03-18", "categories": []}
{"id": "2406.08396", "title": "Neural Blind Source Separation and Diarization for Distant Speech\n  Recognition", "abstract": "  This paper presents a neural method for distant speech recognition (DSR) that\njointly separates and diarizes speech mixtures without supervision by isolated\nsignals. A standard separation method for multi-talker DSR is a statistical\nmultichannel method called guided source separation (GSS). While GSS does not\nrequire signal-level supervision, it relies on speaker diarization results to\nhandle unknown numbers of active speakers. To overcome this limitation, we\nintroduce and train a neural inference model in a weakly-supervised manner,\nemploying the objective function of a statistical separation method. This\ntraining requires only multichannel mixtures and their temporal annotations of\nspeaker activities. In contrast to GSS, the trained model can jointly separate\nand diarize speech mixtures without any auxiliary information. The experiments\nwith the AMI corpus show that our method outperforms GSS with oracle\ndiarization results regarding word error rates. The code is available online.\n", "date": "2024-06-12", "categories": []}
{"id": "2402.04866", "title": "Room Transfer Function Reconstruction Using Complex-valued Neural\n  Networks and Irregularly Distributed Microphones", "abstract": "  Reconstructing the room transfer functions needed to calculate the complex\nsound field in a room has several important real-world applications. However,\nan unpractical number of microphones is often required. Recently, in addition\nto classical signal processing methods, deep learning techniques have been\napplied to reconstruct the room transfer function starting from a very limited\nset of measurements at scattered points in the room. In this paper, we employ\ncomplex-valued neural networks to estimate room transfer functions in the\nfrequency range of the first room resonances, using a few irregularly\ndistributed microphones. To the best of our knowledge, this is the first time\nthat complex-valued neural networks are used to estimate room transfer\nfunctions. To analyze the benefits of applying complex-valued optimization to\nthe considered task, we compare the proposed technique with a state-of-the-art\nkernel-based signal processing approach for sound field reconstruction, showing\nthat the proposed technique exhibits relevant advantages in terms of phase\naccuracy and overall quality of the reconstructed sound field. For informative\npurposes, we also compare the model with a similarly-structured data-driven\napproach that, however, applies a real-valued neural network to reconstruct\nonly the magnitude of the sound field.\n", "date": "2024-02-01", "categories": []}
{"id": "2406.07250", "title": "Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot\n  Unsupervised Anomalous Sound Detection for Machine Condition Monitoring", "abstract": "  We present the task description of the Detection and Classification of\nAcoustic Scenes and Events (DCASE) 2024 Challenge Task 2: First-shot\nunsupervised anomalous sound detection (ASD) for machine condition monitoring.\nContinuing from last year's DCASE 2023 Challenge Task 2, we organize the task\nas a first-shot problem under domain generalization required settings. The main\ngoal of the first-shot problem is to enable rapid deployment of ASD systems for\nnew kinds of machines without the need for machine-specific hyperparameter\ntunings. This problem setting was realized by (1) giving only one section for\neach machine type and (2) having completely different machine types for the\ndevelopment and evaluation datasets. For the DCASE 2024 Challenge Task 2, data\nof completely new machine types were newly collected and provided as the\nevaluation dataset. In addition, attribute information such as the machine\noperation conditions were concealed for several machine types to mimic\nsituations where such information are unavailable. We will add challenge\nresults and analysis of the submissions after the challenge submission\ndeadline.\n", "date": "2024-06-11", "categories": []}
{"id": "2406.06139", "title": "Thunder : Unified Regression-Diffusion Speech Enhancement with a Single\n  Reverse Step using Brownian Bridge", "abstract": "  Diffusion-based speech enhancement has shown promising results, but can\nsuffer from a slower inference time. Initializing the diffusion process with\nthe enhanced audio generated by a regression-based model can be used to reduce\nthe computational steps required. However, these approaches often necessitate a\nregression model, further increasing the system's complexity. We propose\nThunder, a unified regression-diffusion model that utilizes the Brownian bridge\nprocess which can allow the model to act in both modes. The regression mode can\nbe accessed by setting the diffusion time step closed to 1. However, the\nstandard score-based diffusion modeling does not perform well in this setup due\nto gradient instability. To mitigate this problem, we modify the diffusion\nmodel to predict the clean speech instead of the score function, achieving\ncompetitive performance with a more compact model size and fewer reverse steps.\n", "date": "2024-06-10", "categories": []}
{"id": "2406.03120", "title": "RevRIR: Joint Reverberant Speech and Room Impulse Response Embedding\n  using Contrastive Learning with Application to Room Shape Classification", "abstract": "  This paper focuses on room fingerprinting, a task involving the analysis of\nan audio recording to determine the specific volume and shape of the room in\nwhich it was captured. While it is relatively straightforward to determine the\nbasic room parameters from the Room Impulse Responses (RIR), doing so from a\nspeech signal is a cumbersome task. To address this challenge, we introduce a\ndual-encoder architecture that facilitates the estimation of room parameters\ndirectly from speech utterances. During pre-training, one encoder receives the\nRIR while the other processes the reverberant speech signal. A contrastive loss\nfunction is employed to embed the speech and the acoustic response jointly. In\nthe fine-tuning stage, the specific classification task is trained. In the test\nphase, only the reverberant utterance is available, and its embedding is used\nfor the task of room shape classification. The proposed scheme is extensively\nevaluated using simulated acoustic environments.\n", "date": "2024-06-05", "categories": []}
{"id": "2406.01321", "title": "Sequence-to-Sequence Multi-Modal Speech In-Painting", "abstract": "  Speech in-painting is the task of regenerating missing audio contents using\nreliable context information. Despite various recent studies in multi-modal\nperception of audio in-painting, there is still a need for an effective\ninfusion of visual and auditory information in speech in-painting. In this\npaper, we introduce a novel sequence-to-sequence model that leverages the\nvisual information to in-paint audio signals via an encoder-decoder\narchitecture. The encoder plays the role of a lip-reader for facial recordings\nand the decoder takes both encoder outputs as well as the distorted audio\nspectrograms to restore the original speech. Our model outperforms an\naudio-only speech in-painting model and has comparable results with a recent\nmulti-modal speech in-painter in terms of speech quality and intelligibility\nmetrics for distortions of 300 ms to 1500 ms duration, which proves the\neffectiveness of the introduced multi-modality in speech in-painting.\n", "date": "2024-06-03", "categories": []}
{"id": "2405.06289", "title": "Look Once to Hear: Target Speech Hearing with Noisy Examples", "abstract": "  In crowded settings, the human brain can focus on speech from a target\nspeaker, given prior knowledge of how they sound. We introduce a novel\nintelligent hearable system that achieves this capability, enabling target\nspeech hearing to ignore all interfering speech and noise, but the target\nspeaker. A naive approach is to require a clean speech example to enroll the\ntarget speaker. This is however not well aligned with the hearable application\ndomain since obtaining a clean example is challenging in real world scenarios,\ncreating a unique user interface problem. We present the first enrollment\ninterface where the wearer looks at the target speaker for a few seconds to\ncapture a single, short, highly noisy, binaural example of the target speaker.\nThis noisy example is used for enrollment and subsequent speech extraction in\nthe presence of interfering speakers and noise. Our system achieves a signal\nquality improvement of 7.01 dB using less than 5 seconds of noisy enrollment\naudio and can process 8 ms of audio chunks in 6.24 ms on an embedded CPU. Our\nuser studies demonstrate generalization to real-world static and mobile\nspeakers in previously unseen indoor and outdoor multipath environments.\nFinally, our enrollment interface for noisy examples does not cause performance\ndegradation compared to clean examples, while being convenient and\nuser-friendly. Taking a step back, this paper takes an important step towards\nenhancing the human auditory perception with artificial intelligence. We\nprovide code and data at: https://github.com/vb000/LookOnceToHear.\n", "date": "2024-05-10", "categories": []}
{"id": "2405.19497", "title": "Gaussian Flow Bridges for Audio Domain Transfer with Unpaired Data", "abstract": "  Audio domain transfer is the process of modifying audio signals to match\ncharacteristics of a different domain, while retaining the original content.\nThis paper investigates the potential of Gaussian Flow Bridges, an emerging\napproach in generative modeling, for this problem. The presented framework\naddresses the transport problem across different distributions of audio signals\nthrough the implementation of a series of two deterministic probability flows.\nThe proposed framework facilitates manipulation of the target distribution\nproperties through a continuous control variable, which defines a certain\naspect of the target domain. Notably, this approach does not rely on paired\nexamples for training. To address identified challenges on maintaining the\nspeech content consistent, we recommend a training strategy that incorporates\nchunk-based minibatch Optimal Transport couplings of data samples and noise.\nComparing our unsupervised method with established baselines, we find\ncompetitive performance in tasks of reverberation and distortion manipulation.\nDespite encoutering limitations, the intriguing results obtained in this study\nunderscore potential for further exploration.\n", "date": "2024-05-29", "categories": []}
{"id": "2405.20059", "title": "Spectral Mapping of Singing Voices: U-Net-Assisted Vocal Segmentation", "abstract": "  Separating vocal elements from musical tracks is a longstanding challenge in\naudio signal processing. This study tackles the distinct separation of vocal\ncomponents from musical spectrograms. We employ the Short Time Fourier\nTransform (STFT) to extract audio waves into detailed frequency-time\nspectrograms, utilizing the benchmark MUSDB18 dataset for music separation.\nSubsequently, we implement a UNet neural network to segment the spectrogram\nimage, aiming to delineate and extract singing voice components accurately. We\nachieved noteworthy results in audio source separation using of our U-Net-based\nmodels. The combination of frequency-axis normalization with Min/Max scaling\nand the Mean Absolute Error (MAE) loss function achieved the highest\nSource-to-Distortion Ratio (SDR) of 7.1 dB, indicating a high level of accuracy\nin preserving the quality of the original signal during separation. This setup\nalso recorded impressive Source-to-Interference Ratio (SIR) and\nSource-to-Artifact Ratio (SAR) scores of 25.2 dB and 7.2 dB, respectively.\nThese values significantly outperformed other configurations, particularly\nthose using Quantile-based normalization or a Mean Squared Error (MSE) loss\nfunction. Our source code, model weights, and demo material can be found at the\nproject's GitHub repository: https://github.com/mbrotos/SoundSeg\n", "date": "2024-05-30", "categories": []}
{"id": "2306.00814", "title": "Vocos: Closing the gap between time-domain and Fourier-based neural\n  vocoders for high-quality audio synthesis", "abstract": "  Recent advancements in neural vocoding are predominantly driven by Generative\nAdversarial Networks (GANs) operating in the time-domain. While effective, this\napproach neglects the inductive bias offered by time-frequency representations,\nresulting in reduntant and computionally-intensive upsampling operations.\nFourier-based time-frequency representation is an appealing alternative,\naligning more accurately with human auditory perception, and benefitting from\nwell-established fast algorithms for its computation. Nevertheless, direct\nreconstruction of complex-valued spectrograms has been historically\nproblematic, primarily due to phase recovery issues. This study seeks to close\nthis gap by presenting Vocos, a new model that directly generates Fourier\nspectral coefficients. Vocos not only matches the state-of-the-art in audio\nquality, as demonstrated in our evaluations, but it also substantially improves\ncomputational efficiency, achieving an order of magnitude increase in speed\ncompared to prevailing time-domain neural vocoding approaches. The source code\nand model weights have been open-sourced at https://github.com/gemelo-ai/vocos.\n", "date": "2023-06-01", "categories": []}
{"id": "2402.10009", "title": "Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion", "abstract": "  Editing signals using large pre-trained models, in a zero-shot manner, has\nrecently seen rapid advancements in the image domain. However, this wave has\nyet to reach the audio domain. In this paper, we explore two zero-shot editing\ntechniques for audio signals, which use DDPM inversion with pre-trained\ndiffusion models. The first, which we coin ZEro-shot Text-based Audio (ZETA)\nediting, is adopted from the image domain. The second, named ZEro-shot\nUnSupervized (ZEUS) editing, is a novel approach for discovering semantically\nmeaningful editing directions without supervision. When applied to music\nsignals, this method exposes a range of musically interesting modifications,\nfrom controlling the participation of specific instruments to improvisations on\nthe melody. Samples and code can be found in\nhttps://hilamanor.github.io/AudioEditing/ .\n", "date": "2024-02-15", "categories": []}
{"id": "2405.01242", "title": "TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio\n  and Bone Conduction Speech Super Resolution and Enhancement on Mobile and\n  Wearable Platforms", "abstract": "  We propose TRAMBA, a hybrid transformer and Mamba architecture for acoustic\nand bone conduction speech enhancement, suitable for mobile and wearable\nplatforms. Bone conduction speech enhancement has been impractical to adopt in\nmobile and wearable platforms for several reasons: (i) data collection is\nlabor-intensive, resulting in scarcity; (ii) there exists a performance gap\nbetween state of-art models with memory footprints of hundreds of MBs and\nmethods better suited for resource-constrained systems. To adapt TRAMBA to\nvibration-based sensing modalities, we pre-train TRAMBA with audio speech\ndatasets that are widely available. Then, users fine-tune with a small amount\nof bone conduction data. TRAMBA outperforms state-of-art GANs by up to 7.3% in\nPESQ and 1.8% in STOI, with an order of magnitude smaller memory footprint and\nan inference speed up of up to 465 times. We integrate TRAMBA into real systems\nand show that TRAMBA (i) improves battery life of wearables by up to 160% by\nrequiring less data sampling and transmission; (ii) generates higher quality\nvoice in noisy environments than over-the-air speech; (iii) requires a memory\nfootprint of less than 20.0 MB.\n", "date": "2024-05-02", "categories": []}
{"id": "2207.11749", "title": "Simultaneous source separation of unknown numbers of single-channel\n  underwater acoustic signals based on deep neural networks with\n  separator-decoder structure", "abstract": "  The separation of single-channel underwater acoustic signals is a challenging\nproblem with practical significance. Few existing studies focus on the source\nseparation problem with unknown numbers of signals, and how to evaluate the\nperformance of the systems is not yet clear. In this paper, a deep\nlearning-based simultaneous separating solution with a fixed number of output\nchannels equal to the maximum number of possible targets is proposed to address\nthese two problems. This solution avoids the dimensional disaster caused by the\npermutation problem induced by the alignment of outputs to targets.\nSpecifically, we propose a two-step learning-based separation model with a\nseparator-decoder structure. A performance evaluation method with two\nquantitative metrics of the separation system for situations with mute channels\nin the output channels that do not contain target signals is also proposed.\nExperiments conducted on simulated mixtures of radiated ship noise show that\nthe proposed solution can achieve similar separation performance to that\nattained with a known number of signals. The proposed separation model with\nseparator-decoder structure achieved competitive performance as two models\ndeveloped for known numbers of signals, which is highly explainable and\nextensible and gets the state of the art under this framework.\n", "date": "2022-07-24", "categories": []}
{"id": "2405.15096", "title": "Music Genre Classification: Training an AI model", "abstract": "  Music genre classification is an area that utilizes machine learning models\nand techniques for the processing of audio signals, in which applications range\nfrom content recommendation systems to music recommendation systems. In this\nresearch I explore various machine learning algorithms for the purpose of music\ngenre classification, using features extracted from audio signals.The systems\nare namely, a Multilayer Perceptron (built from scratch), a k-Nearest\nNeighbours (also built from scratch), a Convolutional Neural Network and lastly\na Random Forest wide model. In order to process the audio signals, feature\nextraction methods such as Short-Time Fourier Transform, and the extraction of\nMel Cepstral Coefficients (MFCCs), is performed. Through this extensive\nresearch, I aim to asses the robustness of machine learning models for genre\nclassification, and to compare their results.\n", "date": "2024-05-23", "categories": []}
{"id": "2312.09663", "title": "Toward Deep Drum Source Separation", "abstract": "  In the past, the field of drum source separation faced significant challenges\ndue to limited data availability, hindering the adoption of cutting-edge deep\nlearning methods that have found success in other related audio applications.\nIn this manuscript, we introduce StemGMD, a large-scale audio dataset of\nisolated single-instrument drum stems. Each audio clip is synthesized from MIDI\nrecordings of expressive drums performances using ten real-sounding acoustic\ndrum kits. Totaling 1224 hours, StemGMD is the largest audio dataset of drums\nto date and the first to comprise isolated audio clips for every instrument in\na canonical nine-piece drum kit. We leverage StemGMD to develop LarsNet, a\nnovel deep drum source separation model. Through a bank of dedicated U-Nets,\nLarsNet can separate five stems from a stereo drum mixture faster than\nreal-time and is shown to significantly outperform state-of-the-art nonnegative\nspectro-temporal factorization methods.\n", "date": "2023-12-15", "categories": []}
{"id": "2402.09460", "title": "Unsupervised learning based end-to-end delayless generative fixed-filter\n  active noise control", "abstract": "  Delayless noise control is achieved by our earlier generative fixed-filter\nactive noise control (GFANC) framework through efficient coordination between\nthe co-processor and real-time controller. However, the one-dimensional\nconvolutional neural network (1D CNN) in the co-processor requires initial\ntraining using labelled noise datasets. Labelling noise data can be\nresource-intensive and may introduce some biases. In this paper, we propose an\nunsupervised-GFANC approach to simplify the 1D CNN training process and enhance\nits practicality. During training, the co-processor and real-time controller\nare integrated into an end-to-end differentiable ANC system. This enables us to\nuse the accumulated squared error signal as the loss for training the 1D CNN.\nWith this unsupervised learning paradigm, the unsupervised-GFANC method not\nonly omits the labelling process but also exhibits better noise reduction\nperformance compared to the supervised GFANC method in real noise experiments.\n", "date": "2024-02-08", "categories": []}
{"id": "2305.07489", "title": "Benchmarks and leaderboards for sound demixing tasks", "abstract": "  Music demixing is the task of separating different tracks from the given\nsingle audio signal into components, such as drums, bass, and vocals from the\nrest of the accompaniment. Separation of sources is useful for a range of\nareas, including entertainment and hearing aids. In this paper, we introduce\ntwo new benchmarks for the sound source separation tasks and compare popular\nmodels for sound demixing, as well as their ensembles, on these benchmarks. For\nthe models' assessments, we provide the leaderboard at\nhttps://mvsep.com/quality_checker/, giving a comparison for a range of models.\nThe new benchmark datasets are available for download. We also develop a novel\napproach for audio separation, based on the ensembling of different models that\nare suited best for the particular stem. The proposed solution was evaluated in\nthe context of the Music Demixing Challenge 2023 and achieved top results in\ndifferent tracks of the challenge. The code and the approach are open-sourced\non GitHub.\n", "date": "2023-05-12", "categories": []}
{"id": "2405.04272", "title": "BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion\n  Models", "abstract": "  In this paper, we present an unsupervised single-channel method for joint\nblind dereverberation and room impulse response estimation, based on posterior\nsampling with diffusion models. We parameterize the reverberation operator\nusing a filter with exponential decay for each frequency subband, and\niteratively estimate the corresponding parameters as the speech utterance gets\nrefined along the reverse diffusion trajectory. A measurement consistency\ncriterion enforces the fidelity of the generated speech with the reverberant\nmeasurement, while an unconditional diffusion model implements a strong prior\nfor clean speech generation. Without any knowledge of the room impulse response\nnor any coupled reverberant-anechoic data, we can successfully perform\ndereverberation in various acoustic scenarios. Our method significantly\noutperforms previous blind unsupervised baselines, and we demonstrate its\nincreased robustness to unseen acoustic conditions in comparison to blind\nsupervised methods. Audio samples and code are available online.\n", "date": "2024-05-07", "categories": []}
{"id": "2209.11112", "title": "CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement", "abstract": "  In this work, we further develop the conformer-based metric generative\nadversarial network (CMGAN) model for speech enhancement (SE) in the\ntime-frequency (TF) domain. This paper builds on our previous work but takes a\nmore in-depth look by conducting extensive ablation studies on model inputs and\narchitectural design choices. We rigorously tested the generalization ability\nof the model to unseen noise types and distortions. We have fortified our\nclaims through DNS-MOS measurements and listening tests. Rather than focusing\nexclusively on the speech denoising task, we extend this work to address the\ndereverberation and super-resolution tasks. This necessitated exploring various\narchitectural changes, specifically metric discriminator scores and masking\ntechniques. It is essential to highlight that this is among the earliest works\nthat attempted complex TF-domain super-resolution. Our findings show that CMGAN\noutperforms existing state-of-the-art methods in the three major speech\nenhancement tasks: denoising, dereverberation, and super-resolution. For\nexample, in the denoising task using the Voice Bank+DEMAND dataset, CMGAN\nnotably exceeded the performance of prior models, attaining a PESQ score of\n3.41 and an SSNR of 11.10 dB. Audio samples and CMGAN implementations are\navailable online.\n", "date": "2022-09-22", "categories": []}
{"id": "2404.12725", "title": "Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual\n  Target Speech Extraction", "abstract": "  The integration of visual cues has revitalized the performance of the target\nspeech extraction task, elevating it to the forefront of the field.\nNevertheless, this multi-modal learning paradigm often encounters the challenge\nof modality imbalance. In audio-visual target speech extraction tasks, the\naudio modality tends to dominate, potentially overshadowing the importance of\nvisual guidance. To tackle this issue, we propose AVSepChain, drawing\ninspiration from the speech chain concept. Our approach partitions the\naudio-visual target speech extraction task into two stages: speech perception\nand speech production. In the speech perception stage, audio serves as the\ndominant modality, while visual information acts as the conditional modality.\nConversely, in the speech production stage, the roles are reversed. This\ntransformation of modality status aims to alleviate the problem of modality\nimbalance. Additionally, we introduce a contrastive semantic matching loss to\nensure that the semantic information conveyed by the generated speech aligns\nwith the semantic information conveyed by lip movements during the speech\nproduction stage. Through extensive experiments conducted on multiple benchmark\ndatasets for audio-visual target speech extraction, we showcase the superior\nperformance achieved by our proposed method.\n", "date": "2024-04-19", "categories": []}
{"id": "2404.19615", "title": "SemiPL: A Semi-supervised Method for Event Sound Source Localization", "abstract": "  In recent years, Event Sound Source Localization has been widely applied in\nvarious fields. Recent works typically relying on the contrastive learning\nframework show impressive performance. However, all work is based on large\nrelatively simple datasets. It's also crucial to understand and analyze human\nbehaviors (actions and interactions of people), voices, and sounds in chaotic\nevents in many applications, e.g., crowd management, and emergency response\nservices. In this paper, we apply the existing model to a more complex dataset,\nexplore the influence of parameters on the model, and propose a semi-supervised\nimprovement method SemiPL. With the increase in data quantity and the influence\nof label quality, self-supervised learning will be an unstoppable trend. The\nexperiment shows that the parameter adjustment will positively affect the\nexisting model. In particular, SSPL achieved an improvement of 12.2% cIoU and\n0.56% AUC in Chaotic World compared to the results provided. The code is\navailable at: https://github.com/ly245422/SSPL\n", "date": "2024-04-30", "categories": []}
{"id": "2312.00834", "title": "AV-RIR: Audio-Visual Room Impulse Response Estimation", "abstract": "  Accurate estimation of Room Impulse Response (RIR), which captures an\nenvironment's acoustic properties, is important for speech processing and AR/VR\napplications. We propose AV-RIR, a novel multi-modal multi-task learning\napproach to accurately estimate the RIR from a given reverberant speech signal\nand the visual cues of its corresponding environment. AV-RIR builds on a novel\nneural codec-based architecture that effectively captures environment geometry\nand materials properties and solves speech dereverberation as an auxiliary task\nby using multi-task learning. We also propose Geo-Mat features that augment\nmaterial information into visual cues and CRIP that improves late reverberation\ncomponents in the estimated RIR via image-to-RIR retrieval by 86%. Empirical\nresults show that AV-RIR quantitatively outperforms previous audio-only and\nvisual-only approaches by achieving 36% - 63% improvement across various\nacoustic metrics in RIR estimation. Additionally, it also achieves higher\npreference scores in human evaluation. As an auxiliary benefit, dereverbed\nspeech from AV-RIR shows competitive performance with the state-of-the-art in\nvarious spoken language processing tasks and outperforms reverberation time\nerror score in the real-world AVSpeech dataset. Qualitative examples of both\nsynthesized reverberant speech and enhanced speech can be found at\nhttps://www.youtube.com/watch?v=tTsKhviukAE.\n", "date": "2023-11-30", "categories": []}
{"id": "2404.15296", "title": "Maximum Discrepancy Generative Regularization and Non-Negative Matrix\n  Factorization for Single Channel Source Separation", "abstract": "  The idea of adversarial learning of regularization functionals has recently\nbeen introduced in the wider context of inverse problems. The intuition behind\nthis method is the realization that it is not only necessary to learn the basic\nfeatures that make up a class of signals one wants to represent, but also, or\neven more so, which features to avoid in the representation. In this paper, we\nwill apply this approach to the training of generative models, leading to what\nwe call Maximum Discrepancy Generative Regularization. In particular, we apply\nthis to problem of source separation by means of Non-negative Matrix\nFactorization (NMF) and present a new method for the adversarial training of\nNMF bases. We show in numerical experiments, both for image and audio\nseparation, that this leads to a clear improvement of the reconstructed\nsignals, in particular in the case where little or no strong supervision data\nis available.\n", "date": "2024-03-26", "categories": []}
{"id": "2309.03641", "title": "Spiking Structured State Space Model for Monaural Speech Enhancement", "abstract": "  Speech enhancement seeks to extract clean speech from noisy signals.\nTraditional deep learning methods face two challenges: efficiently using\ninformation in long speech sequences and high computational costs. To address\nthese, we introduce the Spiking Structured State Space Model (Spiking-S4). This\napproach merges the energy efficiency of Spiking Neural Networks (SNN) with the\nlong-range sequence modeling capabilities of Structured State Space Models\n(S4), offering a compelling solution. Evaluation on the DNS Challenge and\nVoiceBank+Demand Datasets confirms that Spiking-S4 rivals existing Artificial\nNeural Network (ANN) methods but with fewer computational resources, as\nevidenced by reduced parameters and Floating Point Operations (FLOPs).\n", "date": "2023-09-07", "categories": []}
{"id": "2404.11116", "title": "Music Enhancement with Deep Filters: A Technical Report for The ICASSP\n  2024 Cadenza Challenge", "abstract": "  In this challenge, we disentangle the deep filters from the original\nDeepfilterNet and incorporate them into our Spec-UNet-based network to further\nimprove a hybrid Demucs (hdemucs) based remixing pipeline. The motivation\nbehind the use of the deep filter component lies at its potential in better\nhandling temporal fine structures. We demonstrate an incremental improvement in\nboth the Signal-to-Distortion Ratio (SDR) and the Hearing Aid Audio Quality\nIndex (HAAQI) metrics when comparing the performance of hdemucs against\ndifferent versions of our model.\n", "date": "2024-04-17", "categories": []}
{"id": "2404.01611", "title": "Audio Simulation for Sound Source Localization in Virtual Evironment", "abstract": "  Non-line-of-sight localization in signal-deprived environments is a\nchallenging yet pertinent problem. Acoustic methods in such predominantly\nindoor scenarios encounter difficulty due to the reverberant nature. In this\nstudy, we aim to locate sound sources to specific locations within a virtual\nenvironment by leveraging physically grounded sound propagation simulations and\nmachine learning methods. This process attempts to overcome the issue of data\ninsufficiency to localize sound sources to their location of occurrence\nespecially in post-event localization. We achieve 0.786+/- 0.0136 F1-score\nusing an audio transformer spectrogram approach.\n", "date": "2024-04-02", "categories": []}
{"id": "2404.01740", "title": "Weakly-supervised Audio Separation via Bi-modal Semantic Similarity", "abstract": "  Conditional sound separation in multi-source audio mixtures without having\naccess to single source sound data during training is a long standing\nchallenge. Existing mix-and-separate based methods suffer from significant\nperformance drop with multi-source training mixtures due to the lack of\nsupervision signal for single source separation cases during training. However,\nin the case of language-conditional audio separation, we do have access to\ncorresponding text descriptions for each audio mixture in our training data,\nwhich can be seen as (rough) representations of the audio samples in the\nlanguage modality. To this end, in this paper, we propose a generic bi-modal\nseparation framework which can enhance the existing unsupervised frameworks to\nseparate single-source signals in a target modality (i.e., audio) using the\neasily separable corresponding signals in the conditioning modality (i.e.,\nlanguage), without having access to single-source samples in the target\nmodality during training. We empirically show that this is well within reach if\nwe have access to a pretrained joint embedding model between the two modalities\n(i.e., CLAP). Furthermore, we propose to incorporate our framework into two\nfundamental scenarios to enhance separation performance. First, we show that\nour proposed methodology significantly improves the performance of purely\nunsupervised baselines by reducing the distribution shift between training and\ntest samples. In particular, we show that our framework can achieve 71% boost\nin terms of Signal-to-Distortion Ratio (SDR) over the baseline, reaching 97.5%\nof the supervised learning performance. Second, we show that we can further\nimprove the performance of the supervised learning itself by 17% if we augment\nit by our proposed weakly-supervised framework, that enables a powerful\nsemi-supervised framework for audio separation.\n", "date": "2024-04-02", "categories": []}
{"id": "2403.20202", "title": "Voice Signal Processing for Machine Learning. The Case of Speaker\n  Isolation", "abstract": "  The widespread use of automated voice assistants along with other recent\ntechnological developments have increased the demand for applications that\nprocess audio signals and human voice in particular. Voice recognition tasks\nare typically performed using artificial intelligence and machine learning\nmodels. Even though end-to-end models exist, properly pre-processing the signal\ncan greatly reduce the complexity of the task and allow it to be solved with a\nsimpler ML model and fewer computational resources. However, ML engineers who\nwork on such tasks might not have a background in signal processing which is an\nentirely different area of expertise.\n  The objective of this work is to provide a concise comparative analysis of\nFourier and Wavelet transforms that are most commonly used as signal\ndecomposition methods for audio processing tasks. Metrics for evaluating speech\nintelligibility are also discussed, namely Scale-Invariant Signal-to-Distortion\nRatio (SI-SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time\nObjective Intelligibility (STOI). The level of detail in the exposition is\nmeant to be sufficient for an ML engineer to make informed decisions when\nchoosing, fine-tuning, and evaluating a decomposition method for a specific ML\nmodel. The exposition contains mathematical definitions of the relevant\nconcepts accompanied with intuitive non-mathematical explanations in order to\nmake the text more accessible to engineers without deep expertise in signal\nprocessing. Formal mathematical definitions and proofs of theorems are\nintentionally omitted in order to keep the text concise.\n", "date": "2024-03-29", "categories": []}
{"id": "2212.10744", "title": "An Audio-Visual Speech Separation Model Inspired by\n  Cortico-Thalamo-Cortical Circuits", "abstract": "  Audio-visual approaches involving visual inputs have laid the foundation for\nrecent progress in speech separation. However, the optimization of the\nconcurrent usage of auditory and visual inputs is still an active research\narea. Inspired by the cortico-thalamo-cortical circuit, in which the sensory\nprocessing mechanisms of different modalities modulate one another via the\nnon-lemniscal sensory thalamus, we propose a novel cortico-thalamo-cortical\nneural network (CTCNet) for audio-visual speech separation (AVSS). First, the\nCTCNet learns hierarchical auditory and visual representations in a bottom-up\nmanner in separate auditory and visual subnetworks, mimicking the functions of\nthe auditory and visual cortical areas. Then, inspired by the large number of\nconnections between cortical regions and the thalamus, the model fuses the\nauditory and visual information in a thalamic subnetwork through top-down\nconnections. Finally, the model transmits this fused information back to the\nauditory and visual subnetworks, and the above process is repeated several\ntimes. The results of experiments on three speech separation benchmark datasets\nshow that CTCNet remarkably outperforms existing AVSS methods with considerably\nfewer parameters. These results suggest that mimicking the anatomical\nconnectome of the mammalian brain has great potential for advancing the\ndevelopment of deep neural networks. Project repo is\nhttps://github.com/JusperLee/CTCNet.\n", "date": "2022-12-21", "categories": []}
{"id": "2403.16331", "title": "Modeling Analog Dynamic Range Compressors using Deep Learning and\n  State-space Models", "abstract": "  We describe a novel approach for developing realistic digital models of\ndynamic range compressors for digital audio production by analyzing their\nanalog prototypes. While realistic digital dynamic compressors are potentially\nuseful for many applications, the design process is challenging because the\ncompressors operate nonlinearly over long time scales. Our approach is based on\nthe structured state space sequence model (S4), as implementing the state-space\nmodel (SSM) has proven to be efficient at learning long-range dependencies and\nis promising for modeling dynamic range compressors. We present in this paper a\ndeep learning model with S4 layers to model the Teletronix LA-2A analog dynamic\nrange compressor. The model is causal, executes efficiently in real time, and\nachieves roughly the same quality as previous deep-learning models but with\nfewer parameters.\n", "date": "2024-03-24", "categories": []}
{"id": "2309.17189", "title": "RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual\n  Speech Separation", "abstract": "  Audio-visual speech separation methods aim to integrate different modalities\nto generate high-quality separated speech, thereby enhancing the performance of\ndownstream tasks such as speech recognition. Most existing state-of-the-art\n(SOTA) models operate in the time domain. However, their overly simplistic\napproach to modeling acoustic features often necessitates larger and more\ncomputationally intensive models in order to achieve SOTA performance. In this\npaper, we present a novel time-frequency domain audio-visual speech separation\nmethod: Recurrent Time-Frequency Separation Network (RTFS-Net), which applies\nits algorithms on the complex time-frequency bins yielded by the Short-Time\nFourier Transform. We model and capture the time and frequency dimensions of\nthe audio independently using a multi-layered RNN along each dimension.\nFurthermore, we introduce a unique attention-based fusion technique for the\nefficient integration of audio and visual information, and a new mask\nseparation approach that takes advantage of the intrinsic spectral nature of\nthe acoustic features for a clearer separation. RTFS-Net outperforms the prior\nSOTA method in both inference speed and separation quality while reducing the\nnumber of parameters by 90% and MACs by 83%. This is the first time-frequency\ndomain audio-visual speech separation method to outperform all contemporary\ntime-domain counterparts.\n", "date": "2023-09-29", "categories": []}
{"id": "2403.14246", "title": "CATSE: A Context-Aware Framework for Causal Target Sound Extraction", "abstract": "  Target Sound Extraction (TSE) focuses on the problem of separating sources of\ninterest, indicated by a user's cue, from the input mixture. Most existing\nsolutions operate in an offline fashion and are not suited to the low-latency\ncausal processing constraints imposed by applications in live-streamed content\nsuch as augmented hearing. We introduce a family of context-aware low-latency\ncausal TSE models suitable for real-time processing. First, we explore the\nutility of context by providing the TSE model with oracle information about\nwhat sound classes make up the input mixture, where the objective of the model\nis to extract one or more sources of interest indicated by the user. Since the\npractical applications of oracle models are limited due to their assumptions,\nwe introduce a composite multi-task training objective involving separation and\nclassification losses. Our evaluation involving single- and multi-source\nextraction shows the benefit of using context information in the model either\nby means of providing full context or via the proposed multi-task training loss\nwithout the need for full context information. Specifically, we show that our\nproposed model outperforms size- and latency-matched Waveformer, a\nstate-of-the-art model for real-time TSE.\n", "date": "2024-03-21", "categories": []}
{"id": "2302.02257", "title": "Multi-Source Diffusion Models for Simultaneous Music Generation and\n  Separation", "abstract": "  In this work, we define a diffusion-based generative model capable of both\nmusic synthesis and source separation by learning the score of the joint\nprobability density of sources sharing a context. Alongside the classic total\ninference tasks (i.e., generating a mixture, separating the sources), we also\nintroduce and experiment on the partial generation task of source imputation,\nwhere we generate a subset of the sources given the others (e.g., play a piano\ntrack that goes well with the drums). Additionally, we introduce a novel\ninference method for the separation task based on Dirac likelihood functions.\nWe train our model on Slakh2100, a standard dataset for musical source\nseparation, provide qualitative results in the generation settings, and\nshowcase competitive quantitative results in the source separation setting. Our\nmethod is the first example of a single model that can handle both generation\nand separation tasks, thus representing a step toward general audio models.\n", "date": "2023-02-04", "categories": []}
{"id": "2402.19290", "title": "Estimation and Deconvolution of Second Order Cyclostationary Signals", "abstract": "  This method solves the dual problem of blind deconvolution and estimation of\nthe time waveform of noisy second-order cyclo-stationary (CS2) signals that\ntraverse a Transfer Function (TF) en route to a sensor. We have proven that the\ndeconvolution filter exists and eliminates the TF effect from signals whose\nstatistics vary over time. This method is blind, meaning it does not require\nprior knowledge about the signals or TF. Simulations demonstrate the algorithm\nhigh precision across various signal types, TFs, and Signal-to-Noise Ratios\n(SNRs). In this study, the CS2 signals family is restricted to the product of a\ndeterministic periodic function and white noise. Furthermore, this method has\nthe potential to improve the training of Machine Learning models where the\naggregation of signals from identical systems but with different TFs is\nrequired.\n", "date": "2024-02-29", "categories": []}
{"id": "2306.08510", "title": "Permutation Invariant Recurrent Neural Networks for Sound Source\n  Tracking Applications", "abstract": "  Many multi-source localization and tracking models based on neural networks\nuse one or several recurrent layers at their final stages to track the movement\nof the sources. Conventional recurrent neural networks (RNNs), such as the long\nshort-term memories (LSTMs) or the gated recurrent units (GRUs), take a vector\nas their input and use another vector to store their state. However, this\napproach results in the information from all the sources being contained in a\nsingle ordered vector, which is not optimal for permutation-invariant problems\nsuch as multi-source tracking. In this paper, we present a new recurrent\narchitecture that uses unordered sets to represent both its input and its state\nand that is invariant to the permutations of the input set and equivariant to\nthe permutations of the state set. Hence, the information of every sound source\nis represented in an individual embedding and the new estimates are assigned to\nthe tracked trajectories regardless of their order.\n", "date": "2023-06-14", "categories": []}
{"id": "2402.17701", "title": "Real-time Low-latency Music Source Separation using Hybrid\n  Spectrogram-TasNet", "abstract": "  There have been significant advances in deep learning for music demixing in\nrecent years. However, there has been little attention given to how these\nneural networks can be adapted for real-time low-latency applications, which\ncould be helpful for hearing aids, remixing audio streams and live shows. In\nthis paper, we investigate the various challenges involved in adapting current\ndemixing models in the literature for this use case. Subsequently, inspired by\nthe Hybrid Demucs architecture, we propose the Hybrid Spectrogram Time-domain\nAudio Separation Network HS-TasNet, which utilises the advantages of spectral\nand waveform domains. For a latency of 23 ms, the HS-TasNet obtains an overall\nsignal-to-distortion ratio (SDR) of 4.65 on the MusDB test set, and increases\nto 5.55 with additional training data. These results demonstrate the potential\nof efficient demixing for real-time low-latency music applications.\n", "date": "2024-02-27", "categories": []}
{"id": "2402.15569", "title": "Toward Fully Self-Supervised Multi-Pitch Estimation", "abstract": "  Multi-pitch estimation is a decades-long research problem involving the\ndetection of pitch activity associated with concurrent musical events within\nmulti-instrument mixtures. Supervised learning techniques have demonstrated\nsolid performance on more narrow characterizations of the task, but suffer from\nlimitations concerning the shortage of large-scale and diverse polyphonic music\ndatasets with multi-pitch annotations. We present a suite of self-supervised\nlearning objectives for multi-pitch estimation, which encourage the\nconcentration of support around harmonics, invariance to timbral\ntransformations, and equivariance to geometric transformations. These\nobjectives are sufficient to train an entirely convolutional autoencoder to\nproduce multi-pitch salience-grams directly, without any fine-tuning. Despite\ntraining exclusively on a collection of synthetic single-note audio samples,\nour fully self-supervised framework generalizes to polyphonic music mixtures,\nand achieves performance comparable to supervised models trained on\nconventional multi-pitch datasets.\n", "date": "2024-02-23", "categories": []}
{"id": "2402.09245", "title": "Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality", "abstract": "  The primary goal of the L3DAS23 Signal Processing Grand Challenge at ICASSP\n2023 is to promote and support collaborative research on machine learning for\n3D audio signal processing, with a specific emphasis on 3D speech enhancement\nand 3D Sound Event Localization and Detection in Extended Reality applications.\nAs part of our latest competition, we provide a brand-new dataset, which\nmaintains the same general characteristics of the L3DAS21 and L3DAS22 datasets,\nbut with first-order Ambisonics recordings from multiple reverberant simulated\nenvironments. Moreover, we start exploring an audio-visual scenario by\nproviding images of these environments, as perceived by the different\nmicrophone positions and orientations. We also propose updated baseline models\nfor both tasks that can now support audio-image couples as input and a\nsupporting API to replicate our results. Finally, we present the results of the\nparticipants. Further details about the challenge are available at\nhttps://www.l3das.com/icassp2023.\n", "date": "2024-02-14", "categories": []}
{"id": "2402.06683", "title": "Sound Source Separation Using Latent Variational Block-Wise\n  Disentanglement", "abstract": "  While neural network approaches have made significant strides in resolving\nclassical signal processing problems, it is often the case that hybrid\napproaches that draw insight from both signal processing and neural networks\nproduce more complete solutions. In this paper, we present a hybrid classical\ndigital signal processing/deep neural network (DSP/DNN) approach to source\nseparation (SS) highlighting the theoretical link between variational\nautoencoder and classical approaches to SS. We propose a system that transforms\nthe single channel under-determined SS task to an equivalent multichannel\nover-determined SS problem in a properly designed latent space. The separation\ntask in the latent space is treated as finding a variational block-wise\ndisentangled representation of the mixture. We show empirically, that the\ndesign choices and the variational formulation of the task at hand motivated by\nthe classical signal processing theoretical results lead to robustness to\nunseen out-of-distribution data and reduction of the overfitting risk. To\naddress the resulting permutation issue we explicitly incorporate a novel\ndifferentiable permutation loss function and augment the model with a memory\nmechanism to keep track of the statistics of the individual sources.\n", "date": "2024-02-08", "categories": []}
{"id": "2402.01571", "title": "Spiking Music: Audio Compression with Event Based Auto-encoders", "abstract": "  Neurons in the brain communicate information via punctual events called\nspikes. The timing of spikes is thought to carry rich information, but it is\nnot clear how to leverage this in digital systems. We demonstrate that\nevent-based encoding is efficient for audio compression. To build this\nevent-based representation we use a deep binary auto-encoder, and under high\nsparsity pressure, the model enters a regime where the binary event matrix is\nstored more efficiently with sparse matrix storage algorithms. We test this on\nthe large MAESTRO dataset of piano recordings against vector quantized\nauto-encoders. Not only does our \"Spiking Music compression\" algorithm achieve\na competitive compression/reconstruction trade-off, but selectivity and\nsynchrony between encoded events and piano key strikes emerge without\nsupervision in the sparse regime.\n", "date": "2024-02-02", "categories": []}
{"id": "2401.06203", "title": "Remixing Music for Hearing Aids Using Ensemble of Fine-Tuned Source\n  Separators", "abstract": "  This paper introduces our system submission for the Cadenza ICASSP 2024 Grand\nChallenge, which presents the problem of remixing and enhancing music for\nhearing aid users. Our system placed first in the challenge, achieving the best\naverage Hearing-Aid Audio Quality Index (HAAQI) score on the evaluation data\nset. We describe the system, which uses an ensemble of deep learning music\nsource separators that are fine tuned on the challenge data. We demonstrate the\neffectiveness of our system through the challenge results and analyze the\nimportance of different system aspects through ablation studies.\n", "date": "2024-01-11", "categories": []}
{"id": "2306.01433", "title": "Blind Audio Bandwidth Extension: A Diffusion-Based Zero-Shot Approach", "abstract": "  Audio bandwidth extension involves the realistic reconstruction of\nhigh-frequency spectra from bandlimited observations. In cases where the\nlowpass degradation is unknown, such as in restoring historical audio\nrecordings, this becomes a blind problem. This paper introduces a novel method\ncalled BABE (Blind Audio Bandwidth Extension) that addresses the blind problem\nin a zero-shot setting, leveraging the generative priors of a pre-trained\nunconditional diffusion model. During the inference process, BABE utilizes a\ngeneralized version of diffusion posterior sampling, where the degradation\noperator is unknown but parametrized and inferred iteratively. The performance\nof the proposed method is evaluated using objective and subjective metrics, and\nthe results show that BABE surpasses state-of-the-art blind bandwidth extension\nbaselines and achieves competitive performance compared to informed methods\nwhen tested with synthetic data. Moreover, BABE exhibits robust generalization\ncapabilities when enhancing real historical recordings, effectively\nreconstructing the missing high-frequency content while maintaining coherence\nwith the original recording. Subjective preference tests confirm that BABE\nsignificantly improves the audio quality of historical music recordings.\nExamples of historical recordings restored with the proposed method are\navailable on the companion webpage:\n(http://research.spa.aalto.fi/publications/papers/ieee-taslp-babe/)\n", "date": "2023-06-02", "categories": []}
{"id": "2401.17129", "title": "Enhanced Sound Event Localization and Detection in Real 360-degree\n  audio-visual soundscapes", "abstract": "  This technical report details our work towards building an enhanced\naudio-visual sound event localization and detection (SELD) network. We build on\ntop of the audio-only SELDnet23 model and adapt it to be audio-visual by\nmerging both audio and video information prior to the gated recurrent unit\n(GRU) of the audio-only network. Our model leverages YOLO and DETIC object\ndetectors. We also build a framework that implements audio-visual data\naugmentation and audio-visual synthetic data generation. We deliver an\naudio-visual SELDnet system that outperforms the existing audio-visual SELD\nbaseline.\n", "date": "2024-01-29", "categories": []}
{"id": "2310.19063", "title": "Feature Aggregation in Joint Sound Classification and Localization\n  Neural Networks", "abstract": "  This study addresses the application of deep learning techniques in joint\nsound signal classification and localization networks. Current state-of-the-art\nsound source localization deep learning networks lack feature aggregation\nwithin their architecture. Feature aggregation enhances model performance by\nenabling the consolidation of information from different feature scales,\nthereby improving feature robustness and invariance. This is particularly\nimportant in SSL networks, which must differentiate direct and indirect\nacoustic signals. To address this gap, we adapt feature aggregation techniques\nfrom computer vision neural networks to signal detection neural networks.\nAdditionally, we propose the Scale Encoding Network (SEN) for feature\naggregation to encode features from various scales, compressing the network for\nmore computationally efficient aggregation. To evaluate the efficacy of feature\naggregation in SSL networks, we integrated the following computer vision\nfeature aggregation sub-architectures into a SSL control architecture: Path\nAggregation Network (PANet), Weighted Bi-directional Feature Pyramid Network\n(BiFPN), and SEN. These sub-architectures were evaluated using two metrics for\nsignal classification and two metrics for direction-of-arrival regression.\nPANet and BiFPN are established aggregators in computer vision models, while\nthe proposed SEN is a more compact aggregator. The results suggest that models\nincorporating feature aggregations outperformed the control model, the Sound\nEvent Localization and Detection network (SELDnet), in both sound signal\nclassification and localization. The feature aggregation techniques enhance the\nperformance of sound detection neural networks, particularly in\ndirection-of-arrival regression.\n", "date": "2023-10-29", "categories": []}
{"id": "2211.01839", "title": "HyperSound: Generating Implicit Neural Representations of Audio Signals\n  with Hypernetworks", "abstract": "  Implicit neural representations (INRs) are a rapidly growing research field,\nwhich provides alternative ways to represent multimedia signals. Recent\napplications of INRs include image super-resolution, compression of\nhigh-dimensional signals, or 3D rendering. However, these solutions usually\nfocus on visual data, and adapting them to the audio domain is not trivial.\nMoreover, it requires a separately trained model for every data sample. To\naddress this limitation, we propose HyperSound, a meta-learning method\nleveraging hypernetworks to produce INRs for audio signals unseen at training\ntime. We show that our approach can reconstruct sound waves with quality\ncomparable to other state-of-the-art models.\n", "date": "2022-11-03", "categories": []}
{"id": "2401.14185", "title": "TDFNet: An Efficient Audio-Visual Speech Separation Model with Top-down\n  Fusion", "abstract": "  Audio-visual speech separation has gained significant traction in recent\nyears due to its potential applications in various fields such as speech\nrecognition, diarization, scene analysis and assistive technologies. Designing\na lightweight audio-visual speech separation network is important for\nlow-latency applications, but existing methods often require higher\ncomputational costs and more parameters to achieve better separation\nperformance. In this paper, we present an audio-visual speech separation model\ncalled Top-Down-Fusion Net (TDFNet), a state-of-the-art (SOTA) model for\naudio-visual speech separation, which builds upon the architecture of TDANet,\nan audio-only speech separation method. TDANet serves as the architectural\nfoundation for the auditory and visual networks within TDFNet, offering an\nefficient model with fewer parameters. On the LRS2-2Mix dataset, TDFNet\nachieves a performance increase of up to 10\\% across all performance metrics\ncompared with the previous SOTA method CTCNet. Remarkably, these results are\nachieved using fewer parameters and only 28\\% of the multiply-accumulate\noperations (MACs) of CTCNet. In essence, our method presents a highly effective\nand efficient solution to the challenges of speech separation within the\naudio-visual domain, making significant strides in harnessing visual\ninformation optimally.\n", "date": "2024-01-25", "categories": []}
{"id": "2211.16028", "title": "JaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus", "abstract": "  We construct a corpus of Japanese a cappella vocal ensembles (jaCappella\ncorpus) for vocal ensemble separation and synthesis. It consists of 35\ncopyright-cleared vocal ensemble songs and their audio recordings of individual\nvoice parts. These songs were arranged from out-of-copyright Japanese\nchildren's songs and have six voice parts (lead vocal, soprano, alto, tenor,\nbass, and vocal percussion). They are divided into seven subsets, each of which\nfeatures typical characteristics of a music genre such as jazz and enka. The\nvariety in genre and voice part match vocal ensembles recently widespread in\nsocial media services such as YouTube, although the main targets of\nconventional vocal ensemble datasets are choral singing made up of soprano,\nalto, tenor, and bass. Experimental evaluation demonstrates that our corpus is\na challenging resource for vocal ensemble separation. Our corpus is available\non our project page (https://tomohikonakamura.github.io/jaCappella_corpus/).\n", "date": "2022-11-29", "categories": []}
{"id": "2401.12238", "title": "Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound\n  Event Localization and Detection in Realistic Rooms", "abstract": "  Sound event localization and detection (SELD) is an important task in machine\nlistening. Major advancements rely on simulated data with sound events in\nspecific rooms and strong spatio-temporal labels. SELD data is simulated by\nconvolving spatialy-localized room impulse responses (RIRs) with sound\nwaveforms to place sound events in a soundscape. However, RIRs require manual\ncollection in specific rooms. We present SpatialScaper, a library for SELD data\nsimulation and augmentation. Compared to existing tools, SpatialScaper emulates\nvirtual rooms via parameters such as size and wall absorption. This allows for\nparameterized placement (including movement) of foreground and background sound\nsources. SpatialScaper also includes data augmentation pipelines that can be\napplied to existing SELD data. As a case study, we use SpatialScaper to add\nrooms to the DCASE SELD data. Training a model with our data led to progressive\nperformance improves as a direct function of acoustic diversity. These results\nshow that SpatialScaper is valuable to train robust SELD models.\n", "date": "2024-01-19", "categories": []}
{"id": "2401.12068", "title": "Resource-constrained stereo singing voice cancellation", "abstract": "  We study the problem of stereo singing voice cancellation, a subtask of music\nsource separation, whose goal is to estimate an instrumental background from a\nstereo mix. We explore how to achieve performance similar to large\nstate-of-the-art source separation networks starting from a small, efficient\nmodel for real-time speech separation. Such a model is useful when memory and\ncompute are limited and singing voice processing has to run with limited\nlook-ahead. In practice, this is realised by adapting an existing mono model to\nhandle stereo input. Improvements in quality are obtained by tuning model\nparameters and expanding the training set. Moreover, we highlight the benefits\na stereo model brings by introducing a new metric which detects attenuation\ninconsistencies between channels. Our approach is evaluated using objective\noffline metrics and a large-scale MUSHRA trial, confirming the effectiveness of\nour techniques in stringent listening tests.\n", "date": "2024-01-22", "categories": []}
{"id": "2306.14411", "title": "Score-based Source Separation with Applications to Digital Communication\n  Signals", "abstract": "  We propose a new method for separating superimposed sources using\ndiffusion-based generative models. Our method relies only on separately trained\nstatistical priors of independent sources to establish a new objective function\nguided by maximum a posteriori estimation with an $\\alpha$-posterior, across\nmultiple levels of Gaussian smoothing. Motivated by applications in\nradio-frequency (RF) systems, we are interested in sources with underlying\ndiscrete nature and the recovery of encoded bits from a signal of interest, as\nmeasured by the bit error rate (BER). Experimental results with RF mixtures\ndemonstrate that our method results in a BER reduction of 95% over classical\nand existing learning-based methods. Our analysis demonstrates that our\nproposed method yields solutions that asymptotically approach the modes of an\nunderlying discrete distribution. Furthermore, our method can be viewed as a\nmulti-source extension to the recently proposed score distillation sampling\nscheme, shedding additional light on its use beyond conditional sampling. The\nproject webpage is available at https://alpha-rgs.github.io\n", "date": "2023-06-26", "categories": []}
{"id": "2401.08268", "title": "An Explainable Proxy Model for Multiabel Audio Segmentation", "abstract": "  Audio signal segmentation is a key task for automatic audio indexing. It\nconsists of detecting the boundaries of class-homogeneous segments in the\nsignal. In many applications, explainable AI is a vital process for\ntransparency of decision-making with machine learning. In this paper, we\npropose an explainable multilabel segmentation model that solves speech\nactivity (SAD), music (MD), noise (ND), and overlapped speech detection (OSD)\nsimultaneously. This proxy uses the non-negative matrix factorization (NMF) to\nmap the embedding used for the segmentation to the frequency domain.\nExperiments conducted on two datasets show similar performances as the\npre-trained black box model while showing strong explainability features.\nSpecifically, the frequency bins used for the decision can be easily identified\nat both the segment level (local explanations) and global level (class\nprototypes).\n", "date": "2024-01-16", "categories": []}
{"id": "2206.09507", "title": "Resource-Efficient Separation Transformer", "abstract": "  Transformers have recently achieved state-of-the-art performance in speech\nseparation. These models, however, are computationally demanding and require a\nlot of learnable parameters. This paper explores Transformer-based speech\nseparation with a reduced computational cost. Our main contribution is the\ndevelopment of the Resource-Efficient Separation Transformer (RE-SepFormer), a\nself-attention-based architecture that reduces the computational burden in two\nways. First, it uses non-overlapping blocks in the latent space. Second, it\noperates on compact latent summaries calculated from each chunk. The\nRE-SepFormer reaches a competitive performance on the popular WSJ0-2Mix and\nWHAM! datasets in both causal and non-causal settings. Remarkably, it scales\nsignificantly better than the previous Transformer-based architectures in terms\nof memory and inference time, making it more suitable for processing long\nmixtures.\n", "date": "2022-06-19", "categories": []}
{"id": "2312.14507", "title": "Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and\n  Spectral Optimal Transport", "abstract": "  In neural audio signal processing, pitch conditioning has been used to\nenhance the performance of synthesizers. However, jointly training pitch\nestimators and synthesizers is a challenge when using standard audio-to-audio\nreconstruction loss, leading to reliance on external pitch trackers. To address\nthis issue, we propose using a spectral loss function inspired by optimal\ntransportation theory that minimizes the displacement of spectral energy. We\nvalidate this approach through an unsupervised autoencoding task that fits a\nharmonic template to harmonic signals. We jointly estimate the fundamental\nfrequency and amplitudes of harmonics using a lightweight encoder and\nreconstruct the signals using a differentiable harmonic synthesizer. The\nproposed approach offers a promising direction for improving unsupervised\nparameter estimation in neural audio applications.\n", "date": "2023-12-22", "categories": []}
{"id": "2401.06913", "title": "Microphone Conversion: Mitigating Device Variability in Sound Event\n  Classification", "abstract": "  In this study, we introduce a new augmentation technique to enhance the\nresilience of sound event classification (SEC) systems against device\nvariability through the use of CycleGAN. We also present a unique dataset to\nevaluate this method. As SEC systems become increasingly common, it is crucial\nthat they work well with audio from diverse recording devices. Our method\naddresses limited device diversity in training data by enabling unpaired\ntraining to transform input spectrograms as if they are recorded on a different\ndevice. Our experiments show that our approach outperforms existing methods in\ngeneralization by 5.2% - 11.5% in weighted f1 score. Additionally, it surpasses\nthe current methods in adaptability across diverse recording devices by\nachieving a 6.5% - 12.8% improvement in weighted f1 score.\n", "date": "2024-01-12", "categories": []}
{"id": "2304.12023", "title": "Multi-channel Speech Separation Using Spatially Selective Deep\n  Non-linear Filters", "abstract": "  In a multi-channel separation task with multiple speakers, we aim to recover\nall individual speech signals from the mixture. In contrast to single-channel\napproaches, which rely on the different spectro-temporal characteristics of the\nspeech signals, multi-channel approaches should additionally utilize the\ndifferent spatial locations of the sources for a more powerful separation\nespecially when the number of sources increases. To enhance the spatial\nprocessing in a multi-channel source separation scenario, in this work, we\npropose a deep neural network (DNN) based spatially selective filter (SSF) that\ncan be spatially steered to extract the speaker of interest by initializing a\nrecurrent neural network layer with the target direction. We compare the\nproposed SSF with a common end-to-end direct separation (DS) approach trained\nusing utterance-wise permutation invariant training (PIT), which only\nimplicitly learns to perform spatial filtering. We show that the SSF has a\nclear advantage over a DS approach with the same underlying network\narchitecture when there are more than two speakers in the mixture, which can be\nattributed to a better use of the spatial information. Furthermore, we find\nthat the SSF generalizes much better to additional noise sources that were not\nseen during training and to scenarios with speakers positioned at a similar\nangle.\n", "date": "2023-04-24", "categories": []}
{"id": "2401.04558", "title": "HyperGANStrument: Instrument Sound Synthesis and Editing with\n  Pitch-Invariant Hypernetworks", "abstract": "  GANStrument, exploiting GANs with a pitch-invariant feature extractor and\ninstance conditioning technique, has shown remarkable capabilities in\nsynthesizing realistic instrument sounds. To further improve the reconstruction\nability and pitch accuracy to enhance the editability of user-provided sound,\nwe propose HyperGANStrument, which introduces a pitch-invariant hypernetwork to\nmodulate the weights of a pre-trained GANStrument generator, given a one-shot\nsound as input. The hypernetwork modulation provides feedback for the generator\nin the reconstruction of the input sound. In addition, we take advantage of an\nadversarial fine-tuning scheme for the hypernetwork to improve the\nreconstruction fidelity and generation diversity of the generator. Experimental\nresults show that the proposed model not only enhances the generation\ncapability of GANStrument but also significantly improves the editability of\nsynthesized sounds. Audio examples are available at the online demo page.\n", "date": "2024-01-09", "categories": []}
{"id": "2308.12770", "title": "WavMark: Watermarking for Audio Generation", "abstract": "  Recent breakthroughs in zero-shot voice synthesis have enabled imitating a\nspeaker's voice using just a few seconds of recording while maintaining a high\nlevel of realism. Alongside its potential benefits, this powerful technology\nintroduces notable risks, including voice fraud and speaker impersonation.\nUnlike the conventional approach of solely relying on passive methods for\ndetecting synthetic data, watermarking presents a proactive and robust defence\nmechanism against these looming risks. This paper introduces an innovative\naudio watermarking framework that encodes up to 32 bits of watermark within a\nmere 1-second audio snippet. The watermark is imperceptible to human senses and\nexhibits strong resilience against various attacks. It can serve as an\neffective identifier for synthesized voices and holds potential for broader\napplications in audio copyright protection. Moreover, this framework boasts\nhigh flexibility, allowing for the combination of multiple watermark segments\nto achieve heightened robustness and expanded capacity. Utilizing 10 to\n20-second audio as the host, our approach demonstrates an average Bit Error\nRate (BER) of 0.48\\% across ten common attacks, a remarkable reduction of over\n2800\\% in BER compared to the state-of-the-art watermarking tool. See\nhttps://aka.ms/wavmark for demos of our work.\n", "date": "2023-08-24", "categories": []}
{"id": "2312.13143", "title": "Underwater Acoustic Signal Recognition Based on Salient Feature", "abstract": "  With the rapid advancement of technology, the recognition of underwater\nacoustic signals in complex environments has become increasingly crucial.\nCurrently, mainstream underwater acoustic signal recognition relies primarily\non time-frequency analysis to extract spectral features, finding widespread\napplications in the field. However, existing recognition methods heavily depend\non expert systems, facing limitations such as restricted knowledge bases and\nchallenges in handling complex relationships. These limitations stem from the\ncomplexity and maintenance difficulties associated with rules or inference\nengines. Recognizing the potential advantages of deep learning in handling\nintricate relationships, this paper proposes a method utilizing neural networks\nfor underwater acoustic signal recognition. The proposed approach involves\ncontinual learning of features extracted from spectra for the classification of\nunderwater acoustic signals. Deep learning models can automatically learn\nabstract features from data and continually adjust weights during training to\nenhance classification performance.\n", "date": "2023-12-20", "categories": []}
{"id": "2308.07170", "title": "Human Voice Pitch Estimation: A Convolutional Network with Auto-Labeled\n  and Synthetic Data", "abstract": "  In the domain of music and sound processing, pitch extraction plays a pivotal\nrole. Our research presents a specialized convolutional neural network designed\nfor pitch extraction, particularly from the human singing voice in acapella\nperformances. Notably, our approach combines synthetic data with auto-labeled\nacapella sung audio, creating a robust training environment. Evaluation across\ndatasets comprising synthetic sounds, opera recordings, and time-stretched\nvowels demonstrates its efficacy. This work paves the way for enhanced pitch\nextraction in both music and voice settings.\n", "date": "2023-08-14", "categories": []}
{"id": "2312.03455", "title": "Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence\n  of Training Data", "abstract": "  Perceptual metrics are traditionally used to evaluate the quality of natural\nsignals, such as images and audio. They are designed to mimic the perceptual\nbehaviour of human observers and usually reflect structures found in natural\nsignals. This motivates their use as loss functions for training generative\nmodels such that models will learn to capture the structure held in the metric.\nWe take this idea to the extreme in the audio domain by training a compressive\nautoencoder to reconstruct uniform noise, in lieu of natural data. We show that\ntraining with perceptual losses improves the reconstruction of spectrograms and\nre-synthesized audio at test time over models trained with a standard Euclidean\nloss. This demonstrates better generalisation to unseen natural signals when\nusing perceptual metrics.\n", "date": "2023-12-06", "categories": []}
{"id": "2311.15959", "title": "CheapNET: Improving Light-weight speech enhancement network by projected\n  loss function", "abstract": "  Noise suppression and echo cancellation are critical in speech enhancement\nand essential for smart devices and real-time communication. Deployed in voice\nprocessing front-ends and edge devices, these algorithms must ensure efficient\nreal-time inference with low computational demands. Traditional edge-based\nnoise suppression often uses MSE-based amplitude spectrum mask training, but\nthis approach has limitations. We introduce a novel projection loss function,\ndiverging from MSE, to enhance noise suppression. This method uses projection\ntechniques to isolate key audio components from noise, significantly improving\nmodel performance. For echo cancellation, the function enables direct\npredictions on LAEC pre-processed outputs, substantially enhancing performance.\nOur noise suppression model achieves near state-of-the-art results with only\n3.1M parameters and 0.4GFlops/s computational load. Moreover, our echo\ncancellation model outperforms replicated industry-leading models, introducing\na new perspective in speech enhancement.\n", "date": "2023-11-27", "categories": []}
{"id": "2309.13972", "title": "Audio classification with Dilated Convolution with Learnable Spacings", "abstract": "  Dilated convolution with learnable spacings (DCLS) is a recent convolution\nmethod in which the positions of the kernel elements are learned throughout\ntraining by backpropagation. Its interest has recently been demonstrated in\ncomputer vision (ImageNet classification and downstream tasks). Here we show\nthat DCLS is also useful for audio tagging using the AudioSet classification\nbenchmark. We took two state-of-the-art convolutional architectures using\ndepthwise separable convolutions (DSC), ConvNeXt and ConvFormer, and a hybrid\none using attention in addition, FastViT, and drop-in replaced all the DSC\nlayers by DCLS ones. This significantly improved the mean average precision\n(mAP) with the three architectures without increasing the number of parameters\nand with only a low cost on the throughput. The method code is based on PyTorch\nand is available at https://github.com/K-H-Ismail/DCLS-Audio\n", "date": "2023-09-25", "categories": []}
{"id": "2311.12199", "title": "Improving Label Assignments Learning by Dynamic Sample Dropout Combined\n  with Layer-wise Optimization in Speech Separation", "abstract": "  In supervised speech separation, permutation invariant training (PIT) is\nwidely used to handle label ambiguity by selecting the best permutation to\nupdate the model. Despite its success, previous studies showed that PIT is\nplagued by excessive label assignment switching in adjacent epochs, impeding\nthe model to learn better label assignments. To address this issue, we propose\na novel training strategy, dynamic sample dropout (DSD), which considers\nprevious best label assignments and evaluation metrics to exclude the samples\nthat may negatively impact the learned label assignments during training.\nAdditionally, we include layer-wise optimization (LO) to improve the\nperformance by solving layer-decoupling. Our experiments showed that combining\nDSD and LO outperforms the baseline and solves excessive label assignment\nswitching and layer-decoupling issues. The proposed DSD and LO approach is easy\nto implement, requires no extra training sets or steps, and shows generality to\nvarious speech separation tasks.\n", "date": "2023-11-20", "categories": []}
{"id": "2311.07534", "title": "Unsupervised Musical Object Discovery from Audio", "abstract": "  Current object-centric learning models such as the popular SlotAttention\narchitecture allow for unsupervised visual scene decomposition. Our novel\nMusicSlots method adapts SlotAttention to the audio domain, to achieve\nunsupervised music decomposition. Since concepts of opacity and occlusion in\nvision have no auditory analogues, the softmax normalization of alpha masks in\nthe decoders of visual object-centric models is not well-suited for decomposing\naudio objects. MusicSlots overcomes this problem. We introduce a\nspectrogram-based multi-object music dataset tailored to evaluate\nobject-centric learning on western tonal music. MusicSlots achieves good\nperformance on unsupervised note discovery and outperforms several established\nbaselines on supervised note property prediction tasks.\n", "date": "2023-11-13", "categories": []}
{"id": "2311.07630", "title": "Cross-modal Generative Model for Visual-Guided Binaural Stereo\n  Generation", "abstract": "  Binaural stereo audio is recorded by imitating the way the human ear receives\nsound, which provides people with an immersive listening experience. Existing\napproaches leverage autoencoders and directly exploit visual spatial\ninformation to synthesize binaural stereo, resulting in a limited\nrepresentation of visual guidance. For the first time, we propose a visually\nguided generative adversarial approach for generating binaural stereo audio\nfrom mono audio. Specifically, we develop a Stereo Audio Generation Model\n(SAGM), which utilizes shared spatio-temporal visual information to guide the\ngenerator and the discriminator to work separately. The shared visual\ninformation is updated alternately in the generative adversarial stage,\nallowing the generator and discriminator to deliver their respective guided\nknowledge while visually sharing. The proposed method learns bidirectional\ncomplementary visual information, which facilitates the expression of visual\nguidance in generation. In addition, spatial perception is a crucial attribute\nof binaural stereo audio, and thus the evaluation of stereo spatial perception\nis essential. However, previous metrics failed to measure the spatial\nperception of audio. To this end, a metric to measure the spatial perception of\naudio is proposed for the first time. The proposed metric is capable of\nmeasuring the magnitude and direction of spatial perception in the temporal\ndimension. Further, considering its function, it is feasible to utilize it\ninstead of demanding user studies to some extent. The proposed method achieves\nstate-of-the-art performance on 2 datasets and 5 evaluation metrics.\nQualitative experiments and user studies demonstrate that the method generates\nspace-realistic stereo audio.\n", "date": "2023-11-13", "categories": []}
{"id": "2311.06285", "title": "Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and\n  Audio", "abstract": "  While 3D human body modeling has received much attention in computer vision,\nmodeling the acoustic equivalent, i.e. modeling 3D spatial audio produced by\nbody motion and speech, has fallen short in the community. To close this gap,\nwe present a model that can generate accurate 3D spatial audio for full human\nbodies. The system consumes, as input, audio signals from headset microphones\nand body pose, and produces, as output, a 3D sound field surrounding the\ntransmitter's body, from which spatial audio can be rendered at any arbitrary\nposition in the 3D space. We collect a first-of-its-kind multimodal dataset of\nhuman bodies, recorded with multiple cameras and a spherical array of 345\nmicrophones. In an empirical evaluation, we demonstrate that our model can\nproduce accurate body-induced sound fields when trained with a suitable loss.\nDataset and code are available online.\n", "date": "2023-11-01", "categories": []}
{"id": "2301.10587", "title": "On Batching Variable Size Inputs for Training End-to-End Speech\n  Enhancement Systems", "abstract": "  The performance of neural network-based speech enhancement systems is\nprimarily influenced by the model architecture, whereas training times and\ncomputational resource utilization are primarily affected by training\nparameters such as the batch size. Since noisy and reverberant speech mixtures\ncan have different duration, a batching strategy is required to handle variable\nsize inputs during training, in particular for state-of-the-art end-to-end\nsystems. Such strategies usually strive for a compromise between zero-padding\nand data randomization, and can be combined with a dynamic batch size for a\nmore consistent amount of data in each batch. However, the effect of these\nstrategies on resource utilization and more importantly network performance is\nnot well documented. This paper systematically investigates the effect of\ndifferent batching strategies and batch sizes on the training statistics and\nspeech enhancement performance of a Conv-TasNet, evaluated in both matched and\nmismatched conditions. We find that using a small batch size during training\nimproves performance in both conditions for all batching strategies. Moreover,\nusing sorted or bucket batching with a dynamic batch size allows for reduced\ntraining time and GPU memory usage while achieving similar performance compared\nto random batching with a fixed batch size.\n", "date": "2023-01-25", "categories": []}
{"id": "2308.02560", "title": "From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion", "abstract": "  Deep generative models can generate high-fidelity audio conditioned on\nvarious types of representations (e.g., mel-spectrograms, Mel-frequency\nCepstral Coefficients (MFCC)). Recently, such models have been used to\nsynthesize audio waveforms conditioned on highly compressed representations.\nAlthough such methods produce impressive results, they are prone to generate\naudible artifacts when the conditioning is flawed or imperfect. An alternative\nmodeling approach is to use diffusion models. However, these have mainly been\nused as speech vocoders (i.e., conditioned on mel-spectrograms) or generating\nrelatively low sampling rate signals. In this work, we propose a high-fidelity\nmulti-band diffusion-based framework that generates any type of audio modality\n(e.g., speech, music, environmental sounds) from low-bitrate discrete\nrepresentations. At equal bit rate, the proposed approach outperforms\nstate-of-the-art generative techniques in terms of perceptual quality. Training\nand, evaluation code, along with audio samples, are available on the\nfacebookresearch/audiocraft Github page.\n", "date": "2023-08-02", "categories": []}
{"id": "2311.00320", "title": "Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables", "abstract": "  Imagine being able to listen to the birds chirping in a park without hearing\nthe chatter from other hikers, or being able to block out traffic noise on a\nbusy street while still being able to hear emergency sirens and car honks. We\nintroduce semantic hearing, a novel capability for hearable devices that\nenables them to, in real-time, focus on, or ignore, specific sounds from\nreal-world environments, while also preserving the spatial cues. To achieve\nthis, we make two technical contributions: 1) we present the first neural\nnetwork that can achieve binaural target sound extraction in the presence of\ninterfering sounds and background noise, and 2) we design a training\nmethodology that allows our system to generalize to real-world use. Results\nshow that our system can operate with 20 sound classes and that our\ntransformer-based network has a runtime of 6.56 ms on a connected smartphone.\nIn-the-wild evaluation with participants in previously unseen indoor and\noutdoor scenarios shows that our proof-of-concept system can extract the target\nsounds and generalize to preserve the spatial cues in its binaural output.\nProject page with code: https://semantichearing.cs.washington.edu\n", "date": "2023-11-01", "categories": []}
{"id": "2311.00535", "title": "Active Noise Control Portable Device Design", "abstract": "  While our world is filled with its own natural sounds that we can't resist\nenjoying, it is also chock-full of other sounds that can be irritating, this is\nnoise. Noise not only influences the working efficiency but also the human's\nhealth. The problem of reducing noise is one of great importance and great\ndifficulty. The problem has been addressed in many ways over the years. The\ncurrent methods for noise reducing mostly rely on the materials and\ntransmission medium, which are only effective to some extent for the high\nfrequency noise. However, the effective reduction noise method especially for\nlow frequency noise is very limited.\n  Here we come up with a noise reduction system consist of a sensor to detect\nthe noise in the environment. Then the noise will be sent to an electronic\ncontrol system to process the noise, which will generate a reverse phase\nfrequency signal to counteract the disturbance. Finally, the processed smaller\nnoise will be broadcasted by the speaker. Through this smart noise reduction\nsystem, even the noise with low-frequency can be eliminated.\n  The system is also integrated with sleep tracking and music player\napplications. It can also remember and store settings for the same environment,\nsense temperature, and smart control of home furniture, fire alarm, etc. This\nsmart system can transfer data easily by Wi-Fi or Bluetooth and controlled by\nits APP.\n  In this project, we will present a model of the above technology which can be\nused in various environments to prevent noise pollution and provide a solution\nto the people who have difficulties finding a peaceful and quiet environment\nfor sleep, work or study.\n", "date": "2023-11-01", "categories": []}
{"id": "2310.20446", "title": "LAVSS: Location-Guided Audio-Visual Spatial Audio Separation", "abstract": "  Existing machine learning research has achieved promising results in monaural\naudio-visual separation (MAVS). However, most MAVS methods purely consider what\nthe sound source is, not where it is located. This can be a problem in VR/AR\nscenarios, where listeners need to be able to distinguish between similar audio\nsources located in different directions. To address this limitation, we have\ngeneralized MAVS to spatial audio separation and proposed LAVSS: a\nlocation-guided audio-visual spatial audio separator. LAVSS is inspired by the\ncorrelation between spatial audio and visual location. We introduce the phase\ndifference carried by binaural audio as spatial cues, and we utilize positional\nrepresentations of sounding objects as additional modality guidance. We also\nleverage multi-level cross-modal attention to perform visual-positional\ncollaboration with audio features. In addition, we adopt a pre-trained monaural\nseparator to transfer knowledge from rich mono sounds to boost spatial audio\nseparation. This exploits the correlation between monaural and binaural\nchannels. Experiments on the FAIR-Play dataset demonstrate the superiority of\nthe proposed LAVSS over existing benchmarks of audio-visual separation. Our\nproject page: https://yyx666660.github.io/LAVSS/.\n", "date": "2023-10-31", "categories": []}
{"id": "2305.20054", "title": "UNSSOR: Unsupervised Neural Speech Separation by Leveraging\n  Over-determined Training Mixtures", "abstract": "  In reverberant conditions with multiple concurrent speakers, each microphone\nacquires a mixture signal of multiple speakers at a different location. In\nover-determined conditions where the microphones out-number speakers, we can\nnarrow down the solutions to speaker images and realize unsupervised speech\nseparation by leveraging each mixture signal as a constraint (i.e., the\nestimated speaker images at a microphone should add up to the mixture).\nEquipped with this insight, we propose UNSSOR, an algorithm for\n$\\textbf{u}$nsupervised $\\textbf{n}$eural $\\textbf{s}$peech\n$\\textbf{s}$eparation by leveraging $\\textbf{o}$ver-determined training\nmixtu$\\textbf{r}$es. At each training step, we feed an input mixture to a deep\nneural network (DNN) to produce an intermediate estimate for each speaker,\nlinearly filter the estimates, and optimize a loss so that, at each microphone,\nthe filtered estimates of all the speakers can add up to the mixture to satisfy\nthe above constraint. We show that this loss can promote unsupervised\nseparation of speakers. The linear filters are computed in each sub-band based\non the mixture and DNN estimates through the forward convolutive prediction\n(FCP) algorithm. To address the frequency permutation problem incurred by using\nsub-band FCP, a loss term based on minimizing intra-source magnitude scattering\nis proposed. Although UNSSOR requires over-determined training mixtures, we can\ntrain DNNs to achieve under-determined separation (e.g., unsupervised monaural\nspeech separation). Evaluation results on two-speaker separation in reverberant\nconditions show the effectiveness and potential of UNSSOR.\n", "date": "2023-05-31", "categories": []}
{"id": "2310.19581", "title": "Seeing Through the Conversation: Audio-Visual Speech Separation based on\n  Diffusion Model", "abstract": "  The objective of this work is to extract target speaker's voice from a\nmixture of voices using visual cues. Existing works on audio-visual speech\nseparation have demonstrated their performance with promising intelligibility,\nbut maintaining naturalness remains a challenge. To address this issue, we\npropose AVDiffuSS, an audio-visual speech separation model based on a diffusion\nmechanism known for its capability in generating natural samples. For an\neffective fusion of the two modalities for diffusion, we also propose a\ncross-attention-based feature fusion mechanism. This mechanism is specifically\ntailored for the speech domain to integrate the phonetic information from\naudio-visual correspondence in speech generation. In this way, the fusion\nprocess maintains the high temporal resolution of the features, without\nexcessive computational requirements. We demonstrate that the proposed\nframework achieves state-of-the-art results on two benchmarks, including\nVoxCeleb2 and LRS3, producing speech with notably better naturalness.\n", "date": "2023-10-30", "categories": []}
{"id": "2306.06546", "title": "High-Fidelity Audio Compression with Improved RVQGAN", "abstract": "  Language models have been successfully used to model natural signals, such as\nimages, speech, and music. A key component of these models is a high quality\nneural compression model that can compress high-dimensional natural signals\ninto lower dimensional discrete tokens. To that end, we introduce a\nhigh-fidelity universal neural audio compression algorithm that achieves ~90x\ncompression of 44.1 KHz audio into tokens at just 8kbps bandwidth. We achieve\nthis by combining advances in high-fidelity audio generation with better vector\nquantization techniques from the image domain, along with improved adversarial\nand reconstruction losses. We compress all domains (speech, environment, music,\netc.) with a single universal model, making it widely applicable to generative\nmodeling of all audio. We compare with competing audio compression algorithms,\nand find our method outperforms them significantly. We provide thorough\nablations for every design choice, as well as open-source code and trained\nmodel weights. We hope our work can lay the foundation for the next generation\nof high-fidelity audio modeling.\n", "date": "2023-06-11", "categories": []}
{"id": "2310.16109", "title": "Complex Image Generation SwinTransformer Network for Audio Denoising", "abstract": "  Achieving high-performance audio denoising is still a challenging task in\nreal-world applications. Existing time-frequency methods often ignore the\nquality of generated frequency domain images. This paper converts the audio\ndenoising problem into an image generation task. We first develop a complex\nimage generation SwinTransformer network to capture more information from the\ncomplex Fourier domain. We then impose structure similarity and detailed loss\nfunctions to generate high-quality images and develop an SDR loss to minimize\nthe difference between denoised and clean audios. Extensive experiments on two\nbenchmark datasets demonstrate that our proposed model is better than\nstate-of-the-art methods.\n", "date": "2023-10-24", "categories": []}
{"id": "2310.13430", "title": "HRTF Interpolation using a Spherical Neural Process Meta-Learner", "abstract": "  Several individualization methods have recently been proposed to estimate a\nsubject's Head-Related Transfer Function (HRTF) using convenient input\nmodalities such as anthropometric measurements or pinnae photographs. There\nexists a need for adaptively correcting the estimation error committed by such\nmethods using a few data point samples from the subject's HRTF, acquired using\nacoustic measurements or perceptual feedback. To this end, we introduce a\nConvolutional Conditional Neural Process meta-learner specialized in HRTF error\ninterpolation. In particular, the model includes a Spherical Convolutional\nNeural Network component to accommodate the spherical geometry of HRTF data. It\nalso exploits potential symmetries between the HRTF's left and right channels\nabout the median axis. In this work, we evaluate the proposed model's\nperformance purely on time-aligned spectrum interpolation grounds under a\nsimplified setup where a generic population-mean HRTF forms the initial\nestimates prior to corrections instead of individualized ones. The trained\nmodel achieves up to 3 dB relative error reduction compared to state-of-the-art\ninterpolation methods despite being trained using only 85 subjects. This\nimprovement translates up to nearly a halving of the data point count required\nto achieve comparable accuracy, in particular from 50 to 28 points to reach an\naverage of -20 dB relative error per interpolated feature. Moreover, we show\nthat the trained model provides well-calibrated uncertainty estimates.\nAccordingly, such estimates can inform the sequential decision problem of\nacquiring as few correcting HRTF data points as needed to meet a desired level\nof HRTF individualization accuracy.\n", "date": "2023-10-20", "categories": []}
{"id": "2310.11713", "title": "Separating Invisible Sounds Toward Universal Audiovisual Scene-Aware\n  Sound Separation", "abstract": "  The audio-visual sound separation field assumes visible sources in videos,\nbut this excludes invisible sounds beyond the camera's view. Current methods\nstruggle with such sounds lacking visible cues. This paper introduces a novel\n\"Audio-Visual Scene-Aware Separation\" (AVSA-Sep) framework. It includes a\nsemantic parser for visible and invisible sounds and a separator for\nscene-informed separation. AVSA-Sep successfully separates both sound types,\nwith joint training and cross-modal alignment enhancing effectiveness.\n", "date": "2023-10-18", "categories": []}
{"id": "2211.11917", "title": "Latent Iterative Refinement for Modular Source Separation", "abstract": "  Traditional source separation approaches train deep neural network models\nend-to-end with all the data available at once by minimizing the empirical risk\non the whole training set. On the inference side, after training the model, the\nuser fetches a static computation graph and runs the full model on some\nspecified observed mixture signal to get the estimated source signals.\nAdditionally, many of those models consist of several basic processing blocks\nwhich are applied sequentially. We argue that we can significantly increase\nresource efficiency during both training and inference stages by reformulating\na model's training and inference procedures as iterative mappings of latent\nsignal representations. First, we can apply the same processing block more than\nonce on its output to refine the input signal and consequently improve\nparameter efficiency. During training, we can follow a block-wise procedure\nwhich enables a reduction on memory requirements. Thus, one can train a very\ncomplicated network structure using significantly less computation compared to\nend-to-end training. During inference, we can dynamically adjust how many\nprocessing blocks and iterations of a specific block an input signal needs\nusing a gating module.\n", "date": "2022-11-22", "categories": []}
{"id": "2310.06125", "title": "On Time Domain Conformer Models for Monaural Speech Separation in Noisy\n  Reverberant Acoustic Environments", "abstract": "  Speech separation remains an important topic for multi-speaker technology\nresearchers. Convolution augmented transformers (conformers) have performed\nwell for many speech processing tasks but have been under-researched for speech\nseparation. Most recent state-of-the-art (SOTA) separation models have been\ntime-domain audio separation networks (TasNets). A number of successful models\nhave made use of dual-path (DP) networks which sequentially process local and\nglobal information. Time domain conformers (TD-Conformers) are an analogue of\nthe DP approach in that they also process local and global context sequentially\nbut have a different time complexity function. It is shown that for realistic\nshorter signal lengths, conformers are more efficient when controlling for\nfeature dimension. Subsampling layers are proposed to further improve\ncomputational efficiency. The best TD-Conformer achieves 14.6 dB and 21.2 dB\nSISDR improvement on the WHAMR and WSJ0-2Mix benchmarks, respectively.\n", "date": "2023-10-09", "categories": []}
{"id": "2310.04791", "title": "Conditional Diffusion Model for Target Speaker Extraction", "abstract": "  We propose DiffSpEx, a generative target speaker extraction method based on\nscore-based generative modelling through stochastic differential equations.\nDiffSpEx deploys a continuous-time stochastic diffusion process in the complex\nshort-time Fourier transform domain, starting from the target speaker source\nand converging to a Gaussian distribution centred on the mixture of sources.\nFor the reverse-time process, a parametrised score function is conditioned on a\ntarget speaker embedding to extract the target speaker from the mixture of\nsources. We utilise ECAPA-TDNN target speaker embeddings and condition the\nscore function alternately on the SDE time embedding and the target speaker\nembedding. The potential of DiffSpEx is demonstrated with the WSJ0-2mix\ndataset, achieving an SI-SDR of 12.9 dB and a NISQA score of 3.56. Moreover, we\nshow that fine-tuning a pre-trained DiffSpEx model to a specific speaker\nfurther improves performance, enabling personalisation in target speaker\nextraction.\n", "date": "2023-10-07", "categories": []}
{"id": "2310.05352", "title": "A Glance is Enough: Extract Target Sentence By Looking at A keyword", "abstract": "  This paper investigates the possibility of extracting a target sentence from\nmulti-talker speech using only a keyword as input. For example, in social\nsecurity applications, the keyword might be \"help\", and the goal is to identify\nwhat the person who called for help is articulating while ignoring other\nspeakers. To address this problem, we propose using the Transformer\narchitecture to embed both the keyword and the speech utterance and then rely\non the cross-attention mechanism to select the correct content from the\nconcatenated or overlapping speech. Experimental results on Librispeech\ndemonstrate that our proposed method can effectively extract target sentences\nfrom very noisy and mixed speech (SNR=-3dB), achieving a phone error rate (PER)\nof 26\\%, compared to the baseline system's PER of 96%.\n", "date": "2023-10-09", "categories": []}
{"id": "2310.05799", "title": "The First Cadenza Signal Processing Challenge: Improving Music for Those\n  With a Hearing Loss", "abstract": "  The Cadenza project aims to improve the audio quality of music for those who\nhave a hearing loss. This is being done through a series of signal processing\nchallenges, to foster better and more inclusive technologies. In the first\nround, two common listening scenarios are considered: listening to music over\nheadphones, and with a hearing aid in a car. The first scenario is cast as a\ndemixing-remixing problem, where the music is decomposed into vocals, bass,\ndrums and other components. These can then be intelligently remixed in a\npersonalized way, to increase the audio quality for a person who has a hearing\nloss. In the second scenario, music is coming from car loudspeakers, and the\nmusic has to be enhanced to overcome the masking effect of the car noise. This\nis done by taking into account the music, the hearing ability of the listener,\nthe hearing aid and the speed of the car. The audio quality of the submissions\nwill be evaluated using the Hearing Aid Audio Quality Index (HAAQI) for\nobjective assessment and by a panel of people with hearing loss for subjective\nevaluation.\n", "date": "2023-10-09", "categories": []}
{"id": "2310.05821", "title": "Pre-trained Spatial Priors on Multichannel NMF for Music Source\n  Separation", "abstract": "  This paper presents a novel approach to sound source separation that\nleverages spatial information obtained during the recording setup. Our method\ntrains a spatial mixing filter using solo passages to capture information about\nthe room impulse response and transducer response at each sensor location. This\npre-trained filter is then integrated into a multichannel non-negative matrix\nfactorization (MNMF) scheme to better capture the variances of different sound\nsources. The recording setup used in our experiments is the typical setup for\norchestra recordings, with a main microphone and a close \"cardioid\" or\n\"supercardioid\" microphone for each section of the orchestra. This makes the\nproposed method applicable to many existing recordings. Experiments on\npolyphonic ensembles demonstrate the effectiveness of the proposed framework in\nseparating individual sound sources, improving performance compared to\nconventional MNMF methods.\n", "date": "2023-10-09", "categories": []}
{"id": "2310.04369", "title": "MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice\n  Enhancement", "abstract": "  A typical neural speech enhancement (SE) approach mainly handles speech and\nnoise mixtures, which is not optimal for singing voice enhancement scenarios.\nMusic source separation (MSS) models treat vocals and various accompaniment\ncomponents equally, which may reduce performance compared to the model that\nonly considers vocal enhancement. In this paper, we propose a novel multi-band\ntemporal-frequency neural network (MBTFNet) for singing voice enhancement,\nwhich particularly removes background music, noise and even backing vocals from\nsinging recordings. MBTFNet combines inter and intra-band modeling for better\nprocessing of full-band signals. Dual-path modeling are introduced to expand\nthe receptive field of the model. We propose an implicit personalized\nenhancement (IPE) stage based on signal-to-noise ratio (SNR) estimation, which\nfurther improves the performance of MBTFNet. Experiments show that our proposed\nmodel significantly outperforms several state-of-the-art SE and MSS models.\n", "date": "2023-10-06", "categories": []}
{"id": "2309.05287", "title": "Addressing Feature Imbalance in Sound Source Separation", "abstract": "  Neural networks often suffer from a feature preference problem, where they\ntend to overly rely on specific features to solve a task while disregarding\nother features, even if those neglected features are essential for the task.\nFeature preference problems have primarily been investigated in classification\ntask. However, we observe that feature preference occurs in high-dimensional\nregression task, specifically, source separation. To mitigate feature\npreference in source separation, we propose FEAture BAlancing by Suppressing\nEasy feature (FEABASE). This approach enables efficient data utilization by\nlearning hidden information about the neglected feature. We evaluate our method\nin a multi-channel source separation task, where feature preference between\nspatial feature and timbre feature appears.\n", "date": "2023-09-11", "categories": []}
{"id": "2310.00140", "title": "GASS: Generalizing Audio Source Separation with Large-scale Data", "abstract": "  Universal source separation targets at separating the audio sources of an\narbitrary mix, removing the constraint to operate on a specific domain like\nspeech or music. Yet, the potential of universal source separation is limited\nbecause most existing works focus on mixes with predominantly sound events, and\nsmall training datasets also limit its potential for supervised learning. Here,\nwe study a single general audio source separation (GASS) model trained to\nseparate speech, music, and sound events in a supervised fashion with a\nlarge-scale dataset. We assess GASS models on a diverse set of tasks. Our\nstrong in-distribution results show the feasibility of GASS models, and the\ncompetitive out-of-distribution performance in sound event and speech\nseparation shows its generalization abilities. Yet, it is challenging for GASS\nmodels to generalize for separating out-of-distribution cinematic and music\ncontent. We also fine-tune GASS models on each dataset and consistently\noutperform the ones without pre-training. All fine-tuned models (except the\nmusic separation one) obtain state-of-the-art results in their respective\nbenchmarks.\n", "date": "2023-09-29", "categories": []}
{"id": "2309.17125", "title": "Style Transfer for Non-differentiable Audio Effects", "abstract": "  Digital audio effects are widely used by audio engineers to alter the\nacoustic and temporal qualities of audio data. However, these effects can have\na large number of parameters which can make them difficult to learn for\nbeginners and hamper creativity for professionals. Recently, there have been a\nnumber of efforts to employ progress in deep learning to acquire the low-level\nparameter configurations of audio effects by minimising an objective function\nbetween an input and reference track, commonly referred to as style transfer.\nHowever, current approaches use inflexible black-box techniques or require that\nthe effects under consideration are implemented in an auto-differentiation\nframework. In this work, we propose a deep learning approach to audio\nproduction style matching which can be used with effects implemented in some of\nthe most widely used frameworks, requiring only that the parameters under\nconsideration have a continuous domain. Further, our method includes style\nmatching for various classes of effects, many of which are difficult or\nimpossible to be approximated closely using differentiable functions. We show\nthat our audio embedding approach creates logical encodings of timbral\ninformation, which can be used for a number of downstream tasks. Further, we\nperform a listening test which demonstrates that our approach is able to\nconvincingly style match a multi-band compressor effect.\n", "date": "2023-09-29", "categories": []}
{"id": "2309.15977", "title": "Neural Acoustic Context Field: Rendering Realistic Room Impulse Response\n  With Neural Fields", "abstract": "  Room impulse response (RIR), which measures the sound propagation within an\nenvironment, is critical for synthesizing high-fidelity audio for a given\nenvironment. Some prior work has proposed representing RIR as a neural field\nfunction of the sound emitter and receiver positions. However, these methods do\nnot sufficiently consider the acoustic properties of an audio scene, leading to\nunsatisfactory performance. This letter proposes a novel Neural Acoustic\nContext Field approach, called NACF, to parameterize an audio scene by\nleveraging multiple acoustic contexts, such as geometry, material property, and\nspatial information. Driven by the unique properties of RIR, i.e., temporal\nun-smoothness and monotonic energy attenuation, we design a temporal\ncorrelation module and multi-scale energy decay criterion. Experimental results\nshow that NACF outperforms existing field-based methods by a notable margin.\nPlease visit our project page for more qualitative results.\n", "date": "2023-09-27", "categories": []}
{"id": "2307.14609", "title": "Complete and separate: Conditional separation with missing target source\n  attribute completion", "abstract": "  Recent approaches in source separation leverage semantic information about\ntheir input mixtures and constituent sources that when used in conditional\nseparation models can achieve impressive performance. Most approaches along\nthese lines have focused on simple descriptions, which are not always useful\nfor varying types of input mixtures. In this work, we present an approach in\nwhich a model, given an input mixture and partial semantic information about a\ntarget source, is trained to extract additional semantic data. We then leverage\nthis pre-trained model to improve the separation performance of an uncoupled\nmulti-conditional separation network. Our experiments demonstrate that the\nseparation performance of this multi-conditional model is significantly\nimproved, approaching the performance of an oracle model with complete semantic\ninformation. Furthermore, our approach achieves performance levels that are\ncomparable to those of the best performing specialized single conditional\nmodels, thus providing an easier to use alternative.\n", "date": "2023-07-27", "categories": []}
{"id": "2303.16342", "title": "Language-Guided Audio-Visual Source Separation via Trimodal Consistency", "abstract": "  We propose a self-supervised approach for learning to perform audio source\nseparation in videos based on natural language queries, using only unlabeled\nvideo and audio pairs as training data. A key challenge in this task is\nlearning to associate the linguistic description of a sound-emitting object to\nits visual features and the corresponding components of the audio waveform, all\nwithout access to annotations during training. To overcome this challenge, we\nadapt off-the-shelf vision-language foundation models to provide pseudo-target\nsupervision via two novel loss functions and encourage a stronger alignment\nbetween the audio, visual and natural language modalities. During inference,\nour approach can separate sounds given text, video and audio input, or given\ntext and audio input alone. We demonstrate the effectiveness of our\nself-supervised approach on three audio-visual separation datasets, including\nMUSIC, SOLOS and AudioSet, where we outperform state-of-the-art strongly\nsupervised approaches despite not using object detectors or text labels during\ntraining.\n", "date": "2023-03-28", "categories": []}
{"id": "2309.13537", "title": "Speech enhancement with frequency domain auto-regressive modeling", "abstract": "  Speech applications in far-field real world settings often deal with signals\nthat are corrupted by reverberation. The task of dereverberation constitutes an\nimportant step to improve the audible quality and to reduce the error rates in\napplications like automatic speech recognition (ASR). We propose a unified\nframework of speech dereverberation for improving the speech quality and the\nASR performance using the approach of envelope-carrier decomposition provided\nby an autoregressive (AR) model. The AR model is applied in the frequency\ndomain of the sub-band speech signals to separate the envelope and carrier\nparts. A novel neural architecture based on dual path long short term memory\n(DPLSTM) model is proposed, which jointly enhances the sub-band envelope and\ncarrier components. The dereverberated envelope-carrier signals are modulated\nand the sub-band signals are synthesized to reconstruct the audio signal back.\nThe DPLSTM model for dereverberation of envelope and carrier components also\nallows the joint learning of the network weights for the down stream ASR task.\nIn the ASR tasks on the REVERB challenge dataset as well as on the VOiCES\ndataset, we illustrate that the joint learning of speech dereverberation\nnetwork and the E2E ASR model yields significant performance improvements over\nthe baseline ASR system trained on log-mel spectrogram as well as other\nbenchmarks for dereverberation (average relative improvements of 10-24% over\nthe baseline system). The speech quality improvements, evaluated using\nsubjective listening tests, further highlight the improved quality of the\nreconstructed audio.\n", "date": "2023-09-24", "categories": []}
{"id": "2309.12581", "title": "Sampling-Frequency-Independent Universal Sound Separation", "abstract": "  This paper proposes a universal sound separation (USS) method capable of\nhandling untrained sampling frequencies (SFs). The USS aims at separating\narbitrary sources of different types and can be the key technique to realize a\nsource separator that can be universally used as a preprocessor for any\ndownstream tasks. To realize a universal source separator, there are two\nessential properties: universalities with respect to source types and recording\nconditions. The former property has been studied in the USS literature, which\nhas greatly increased the number of source types that can be handled by a\nsingle neural network. However, the latter property (e.g., SF) has received\nless attention despite its necessity. Since the SF varies widely depending on\nthe downstream tasks, the universal source separator must handle a wide variety\nof SFs. In this paper, to encompass the two properties, we propose an\nSF-independent (SFI) extension of a computationally efficient USS network,\nSuDoRM-RF. The proposed network uses our previously proposed SFI convolutional\nlayers, which can handle various SFs by generating convolutional kernels in\naccordance with an input SF. Experiments show that signal resampling can\ndegrade the USS performance and the proposed method works more consistently\nthan signal-resampling-based methods for various SFs.\n", "date": "2023-09-22", "categories": []}
{"id": "2309.07314", "title": "AudioSR: Versatile Audio Super-resolution at Scale", "abstract": "  Audio super-resolution is a fundamental task that predicts high-frequency\ncomponents for low-resolution audio, enhancing audio quality in digital\napplications. Previous methods have limitations such as the limited scope of\naudio types (e.g., music, speech) and specific bandwidth settings they can\nhandle (e.g., 4kHz to 8kHz). In this paper, we introduce a diffusion-based\ngenerative model, AudioSR, that is capable of performing robust audio\nsuper-resolution on versatile audio types, including sound effects, music, and\nspeech. Specifically, AudioSR can upsample any input audio signal within the\nbandwidth range of 2kHz to 16kHz to a high-resolution audio signal at 24kHz\nbandwidth with a sampling rate of 48kHz. Extensive objective evaluation on\nvarious audio super-resolution benchmarks demonstrates the strong result\nachieved by the proposed model. In addition, our subjective evaluation shows\nthat AudioSR can acts as a plug-and-play module to enhance the generation\nquality of a wide range of audio generative models, including AudioLDM,\nFastspeech2, and MusicGen. Our code and demo are available at\nhttps://audioldm.github.io/audiosr.\n", "date": "2023-09-13", "categories": []}
{"id": "2309.06661", "title": "Sound field decomposition based on two-stage neural networks", "abstract": "  A method for sound field decomposition based on neural networks is proposed.\nThe method comprises two stages: a sound field separation stage and a\nsingle-source localization stage. In the first stage, the sound pressure at\nmicrophones synthesized by multiple sources is separated into one excited by\neach sound source. In the second stage, the source location is obtained as a\nregression from the sound pressure at microphones consisting of a single sound\nsource. The estimated location is not affected by discretization because the\nsecond stage is designed as a regression rather than a classification. Datasets\nare generated by simulation using Green's function, and the neural network is\ntrained for each frequency. Numerical experiments reveal that, compared with\nconventional methods, the proposed method can achieve higher\nsource-localization accuracy and higher sound-field-reconstruction accuracy.\n", "date": "2023-09-13", "categories": []}
{"id": "2309.05975", "title": "CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram", "abstract": "  In this work, we present CleanUNet 2, a speech denoising model that combines\nthe advantages of waveform denoiser and spectrogram denoiser and achieves the\nbest of both worlds. CleanUNet 2 uses a two-stage framework inspired by popular\nspeech synthesis methods that consist of a waveform model and a spectrogram\nmodel. Specifically, CleanUNet 2 builds upon CleanUNet, the state-of-the-art\nwaveform denoiser, and further boosts its performance by taking predicted\nspectrograms from a spectrogram denoiser as the input. We demonstrate that\nCleanUNet 2 outperforms previous methods in terms of various objective and\nsubjective evaluations.\n", "date": "2023-09-12", "categories": []}
{"id": "2308.16540", "title": "Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking\n  in Speech Signals", "abstract": "  In this paper, we propose a new method for the accurate estimation and\ntracking of formants in speech signals using time-varying quasi-closed-phase\n(TVQCP) analysis. Conventional formant tracking methods typically adopt a\ntwo-stage estimate-and-track strategy wherein an initial set of formant\ncandidates are estimated using short-time analysis (e.g., 10--50 ms), followed\nby a tracking stage based on dynamic programming or a linear state-space model.\nOne of the main disadvantages of these approaches is that the tracking stage,\nhowever good it may be, cannot improve upon the formant estimation accuracy of\nthe first stage. The proposed TVQCP method provides a single-stage formant\ntracking that combines the estimation and tracking stages into one. TVQCP\nanalysis combines three approaches to improve formant estimation and tracking:\n(1) it uses temporally weighted quasi-closed-phase analysis to derive\nclosed-phase estimates of the vocal tract with reduced interference from the\nexcitation source, (2) it increases the residual sparsity by using the $L_1$\noptimization and (3) it uses time-varying linear prediction analysis over long\ntime windows (e.g., 100--200 ms) to impose a continuity constraint on the vocal\ntract model and hence on the formant trajectories. Formant tracking experiments\nwith a wide variety of synthetic and natural speech signals show that the\nproposed TVQCP method performs better than conventional and popular formant\ntracking tools, such as Wavesurfer and Praat (based on dynamic programming),\nthe KARMA algorithm (based on Kalman filtering), and DeepFormants (based on\ndeep neural networks trained in a supervised manner). Matlab scripts for the\nproposed method can be found at: https://github.com/njaygowda/ftrack\n", "date": "2023-08-31", "categories": []}
{"id": "2308.16892", "title": "ReZero: Region-customizable Sound Extraction", "abstract": "  We introduce region-customizable sound extraction (ReZero), a general and\nflexible framework for the multi-channel region-wise sound extraction (R-SE)\ntask. R-SE task aims at extracting all active target sounds (e.g., human\nspeech) within a specific, user-defined spatial region, which is different from\nconventional and existing tasks where a blind separation or a fixed, predefined\nspatial region are typically assumed. The spatial region can be defined as an\nangular window, a sphere, a cone, or other geometric patterns. Being a solution\nto the R-SE task, the proposed ReZero framework includes (1) definitions of\ndifferent types of spatial regions, (2) methods for region feature extraction\nand aggregation, and (3) a multi-channel extension of the band-split RNN\n(BSRNN) model specified for the R-SE task. We design experiments for different\nmicrophone array geometries, different types of spatial regions, and\ncomprehensive ablation studies on different system configurations. Experimental\nresults on both simulated and real-recorded data demonstrate the effectiveness\nof ReZero. Demos are available at https://innerselfm.github.io/rezero/.\n", "date": "2023-08-31", "categories": []}
{"id": "2210.17287", "title": "Diffiner: A Versatile Diffusion-based Generative Refiner for Speech\n  Enhancement", "abstract": "  Although deep neural network (DNN)-based speech enhancement (SE) methods\noutperform the previous non-DNN-based ones, they often degrade the perceptual\nquality of generated outputs. To tackle this problem, we introduce a DNN-based\ngenerative refiner, Diffiner, aiming to improve perceptual speech quality\npre-processed by an SE method. We train a diffusion-based generative model by\nutilizing a dataset consisting of clean speech only. Then, our refiner\neffectively mixes clean parts newly generated via denoising diffusion\nrestoration into the degraded and distorted parts caused by a preceding SE\nmethod, resulting in refined speech. Once our refiner is trained on a set of\nclean speech, it can be applied to various SE methods without additional\ntraining specialized for each SE module. Therefore, our refiner can be a\nversatile post-processing module w.r.t. SE methods and has high potential in\nterms of modularity. Experimental results show that our method improved\nperceptual speech quality regardless of the preceding SE methods used.\n", "date": "2022-10-27", "categories": []}
{"id": "2305.11582", "title": "What You Hear Is What You See: Audio Quality Metrics From Image Quality\n  Metrics", "abstract": "  In this study, we investigate the feasibility of utilizing state-of-the-art\nimage perceptual metrics for evaluating audio signals by representing them as\nspectrograms. The encouraging outcome of the proposed approach is based on the\nsimilarity between the neural mechanisms in the auditory and visual pathways.\nFurthermore, we customise one of the metrics which has a psychoacoustically\nplausible architecture to account for the peculiarities of sound signals. We\nevaluate the effectiveness of our proposed metric and several baseline metrics\nusing a music dataset, with promising results in terms of the correlation\nbetween the metrics and the perceived quality of audio as rated by human\nevaluators.\n", "date": "2023-05-19", "categories": []}
{"id": "2308.12370", "title": "AdVerb: Visually Guided Audio Dereverberation", "abstract": "  We present AdVerb, a novel audio-visual dereverberation framework that uses\nvisual cues in addition to the reverberant sound to estimate clean audio.\nAlthough audio-only dereverberation is a well-studied problem, our approach\nincorporates the complementary visual modality to perform audio\ndereverberation. Given an image of the environment where the reverberated sound\nsignal has been recorded, AdVerb employs a novel geometry-aware cross-modal\ntransformer architecture that captures scene geometry and audio-visual\ncross-modal relationship to generate a complex ideal ratio mask, which, when\napplied to the reverberant audio predicts the clean sound. The effectiveness of\nour method is demonstrated through extensive quantitative and qualitative\nevaluations. Our approach significantly outperforms traditional audio-only and\naudio-visual baselines on three downstream tasks: speech enhancement, speech\nrecognition, and speaker verification, with relative improvements in the range\nof 18% - 82% on the LibriSpeech test-clean set. We also achieve highly\nsatisfactory RT60 error scores on the AVSpeech dataset.\n", "date": "2023-08-23", "categories": []}
{"id": "2308.12599", "title": "Exploiting Time-Frequency Conformers for Music Audio Enhancement", "abstract": "  With the proliferation of video platforms on the internet, recording musical\nperformances by mobile devices has become commonplace. However, these\nrecordings often suffer from degradation such as noise and reverberation, which\nnegatively impact the listening experience. Consequently, the necessity for\nmusic audio enhancement (referred to as music enhancement from this point\nonward), involving the transformation of degraded audio recordings into\npristine high-quality music, has surged to augment the auditory experience. To\naddress this issue, we propose a music enhancement system based on the\nConformer architecture that has demonstrated outstanding performance in speech\nenhancement tasks. Our approach explores the attention mechanisms of the\nConformer and examines their performance to discover the best approach for the\nmusic enhancement task. Our experimental results show that our proposed model\nachieves state-of-the-art performance on single-stem music enhancement.\nFurthermore, our system can perform general music enhancement with multi-track\nmixtures, which has not been examined in previous work.\n", "date": "2023-08-24", "categories": []}
{"id": "2308.10388", "title": "Neural Architectures Learning Fourier Transforms, Signal Processing and\n  Much More....", "abstract": "  This report will explore and answer fundamental questions about taking\nFourier Transforms and tying it with recent advances in AI and neural\narchitecture. One interpretation of the Fourier Transform is decomposing a\nsignal into its constituent components by projecting them onto complex\nexponentials. Variants exist, such as discrete cosine transform that does not\noperate on the complex domain and projects an input signal to only cosine\nfunctions oscillating at different frequencies. However, this is a fundamental\nlimitation, and it needs to be more suboptimal. The first one is that all\nkernels are sinusoidal: What if we could have some kernels adapted or learned\naccording to the problem? What if we can use neural architectures for this? We\nshow how one can learn these kernels from scratch for audio signal processing\napplications. We find that the neural architecture not only learns sinusoidal\nkernel shapes but discovers all kinds of incredible signal-processing\nproperties. E.g., windowing functions, onset detectors, high pass filters, low\npass filters, modulations, etc. Further, upon analysis of the filters, we find\nthat the neural architecture has a comb filter-like structure on top of the\nlearned kernels. Comb filters that allow harmonic frequencies to pass through\nare one of the core building blocks/types of filters similar to high-pass,\nlow-pass, and band-pass filters of various traditional signal processing\nalgorithms. Further, we can also use the convolution operation with a signal to\nbe learned from scratch, and we will explore papers in the literature that uses\nthis with that robust Transformer architectures. Further, we would also explore\nmaking the learned kernel's content adaptive, i.e., learning different kernels\nfor different inputs.\n", "date": "2023-08-20", "categories": []}
{"id": "2308.10415", "title": "TokenSplit: Using Discrete Speech Representations for Direct, Refined,\n  and Transcript-Conditioned Speech Separation and Recognition", "abstract": "  We present TokenSplit, a speech separation model that acts on discrete token\nsequences. The model is trained on multiple tasks simultaneously: separate and\ntranscribe each speech source, and generate speech from text. The model\noperates on transcripts and audio token sequences and achieves multiple tasks\nthrough masking of inputs. The model is a sequence-to-sequence encoder-decoder\nmodel that uses the Transformer architecture. We also present a \"refinement\"\nversion of the model that predicts enhanced audio tokens from the audio tokens\nof speech separated by a conventional separation model. Using both objective\nmetrics and subjective MUSHRA listening tests, we show that our model achieves\nexcellent performance in terms of separation, both with or without transcript\nconditioning. We also measure the automatic speech recognition (ASR)\nperformance and provide audio samples of speech synthesis to demonstrate the\nadditional utility of our model.\n", "date": "2023-08-21", "categories": []}
{"id": "2308.10682", "title": "LibriWASN: A Data Set for Meeting Separation, Diarization, and\n  Recognition with Asynchronous Recording Devices", "abstract": "  We present LibriWASN, a data set whose design follows closely the LibriCSS\nmeeting recognition data set, with the marked difference that the data is\nrecorded with devices that are randomly positioned on a meeting table and whose\nsampling clocks are not synchronized. Nine different devices, five smartphones\nwith a single recording channel and four microphone arrays, are used to record\na total of 29 channels. Other than that, the data set follows closely the\nLibriCSS design: the same LibriSpeech sentences are played back from eight\nloudspeakers arranged around a meeting table and the data is organized in\nsubsets with different percentages of speech overlap. LibriWASN is meant as a\ntest set for clock synchronization algorithms, meeting separation, diarization\nand transcription systems on ad-hoc wireless acoustic sensor networks. Due to\nits similarity to LibriCSS, meeting transcription systems developed for the\nformer can readily be tested on LibriWASN. The data set is recorded in two\ndifferent rooms and is complemented with ground-truth diarization information\nof who speaks when.\n", "date": "2023-08-21", "categories": []}
{"id": "2308.07293", "title": "DiffSED: Sound Event Detection with Denoising Diffusion", "abstract": "  Sound Event Detection (SED) aims to predict the temporal boundaries of all\nthe events of interest and their class labels, given an unconstrained audio\nsample. Taking either the splitand-classify (i.e., frame-level) strategy or the\nmore principled event-level modeling approach, all existing methods consider\nthe SED problem from the discriminative learning perspective. In this work, we\nreformulate the SED problem by taking a generative learning perspective.\nSpecifically, we aim to generate sound temporal boundaries from noisy proposals\nin a denoising diffusion process, conditioned on a target audio sample. During\ntraining, our model learns to reverse the noising process by converting noisy\nlatent queries to the groundtruth versions in the elegant Transformer decoder\nframework. Doing so enables the model generate accurate event boundaries from\neven noisy queries during inference. Extensive experiments on the Urban-SED and\nEPIC-Sounds datasets demonstrate that our model significantly outperforms\nexisting alternatives, with 40+% faster convergence in training.\n", "date": "2023-08-14", "categories": []}
{"id": "2308.09514", "title": "Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning", "abstract": "  We present Spatial LibriSpeech, a spatial audio dataset with over 650 hours\nof 19-channel audio, first-order ambisonics, and optional distractor noise.\nSpatial LibriSpeech is designed for machine learning model training, and it\nincludes labels for source position, speaking direction, room acoustics and\ngeometry. Spatial LibriSpeech is generated by augmenting LibriSpeech samples\nwith 200k+ simulated acoustic conditions across 8k+ synthetic rooms. To\ndemonstrate the utility of our dataset, we train models on four spatial audio\ntasks, resulting in a median absolute error of 6.60{\\deg} on 3D source\nlocalization, 0.43m on distance, 90.66ms on T30, and 2.74dB on DRR estimation.\nWe show that the same models generalize well to widely-used evaluation\ndatasets, e.g., obtaining a median absolute error of 12.43{\\deg} on 3D source\nlocalization on TUT Sound Events 2018, and 157.32ms on T30 estimation on ACE\nChallenge.\n", "date": "2023-08-18", "categories": []}
{"id": "2308.04169", "title": "Dual input neural networks for positional sound source localization", "abstract": "  In many signal processing applications, metadata may be advantageously used\nin conjunction with a high dimensional signal to produce a desired output. In\nthe case of classical Sound Source Localization (SSL) algorithms, information\nfrom a high dimensional, multichannel audio signals received by many\ndistributed microphones is combined with information describing acoustic\nproperties of the scene, such as the microphones' coordinates in space, to\nestimate the position of a sound source. We introduce Dual Input Neural\nNetworks (DI-NNs) as a simple and effective way to model these two data types\nin a neural network. We train and evaluate our proposed DI-NN on scenarios of\nvarying difficulty and realism and compare it against an alternative\narchitecture, a classical Least-Squares (LS) method as well as a classical\nConvolutional Recurrent Neural Network (CRNN). Our results show that the DI-NN\nsignificantly outperforms the baselines, achieving a five times lower\nlocalization error than the LS method and two times lower than the CRNN in a\ntest dataset of real recordings.\n", "date": "2023-08-08", "categories": []}
{"id": "2308.03332", "title": "Improving Deep Attractor Network by BGRU and GMM for Speech Separation", "abstract": "  Deep Attractor Network (DANet) is the state-of-the-art technique in speech\nseparation field, which uses Bidirectional Long Short-Term Memory (BLSTM), but\nthe complexity of the DANet model is very high. In this paper, a simplified and\npowerful DANet model is proposed using Bidirectional Gated neural network\n(BGRU) instead of BLSTM. The Gaussian Mixture Model (GMM) other than the\nk-means was applied in DANet as a clustering algorithm to reduce the complexity\nand increase the learning speed and accuracy. The metrics used in this paper\nare Signal to Distortion Ratio (SDR), Signal to Interference Ratio (SIR),\nSignal to Artifact Ratio (SAR), and Perceptual Evaluation Speech Quality (PESQ)\nscore. Two speaker mixture datasets from TIMIT corpus were prepared to evaluate\nthe proposed model, and the system achieved 12.3 dB and 2.94 for SDR and PESQ\nscores respectively, which were better than the original DANet model. Other\nimprovements were 20.7% and 17.9% in the number of parameters and time\ntraining, respectively. The model was applied on mixed Arabic speech signals\nand the results were better than that in English.\n", "date": "2023-08-07", "categories": []}
{"id": "2308.02421", "title": "Differentiable short-time Fourier transform with respect to the hop\n  length", "abstract": "  In this paper, we propose a differentiable version of the short-time Fourier\ntransform (STFT) that allows for gradient-based optimization of the hop length\nor the frame temporal position by making these parameters continuous. Our\napproach provides improved control over the temporal positioning of frames, as\nthe continuous nature of the hop length allows for a more finely-tuned\noptimization. Furthermore, our contribution enables the use of optimization\nmethods such as gradient descent, which are more computationally efficient than\nconventional discrete optimization methods. Our differentiable STFT can also be\neasily integrated into existing algorithms and neural networks. We present a\nsimulated illustration to demonstrate the efficacy of our approach and to\ngarner interest from the research community.\n", "date": "2023-07-26", "categories": []}
{"id": "2308.00426", "title": "Generative adversarial networks with physical sound field priors", "abstract": "  This paper presents a deep learning-based approach for the spatio-temporal\nreconstruction of sound fields using Generative Adversarial Networks (GANs).\nThe method utilises a plane wave basis and learns the underlying statistical\ndistributions of pressure in rooms to accurately reconstruct sound fields from\na limited number of measurements. The performance of the method is evaluated\nusing two established datasets and compared to state-of-the-art methods. The\nresults show that the model is able to achieve an improved reconstruction\nperformance in terms of accuracy and energy retention, particularly in the\nhigh-frequency range and when extrapolating beyond the measurement region.\nFurthermore, the proposed method can handle a varying number of measurement\npositions and configurations without sacrificing performance. The results\nsuggest that this approach provides a promising approach to sound field\nreconstruction using generative models that allow for a physically informed\nprior to acoustics problems.\n", "date": "2023-08-01", "categories": []}
{"id": "2307.15913", "title": "Moisesdb: A dataset for source separation beyond 4-stems", "abstract": "  In this paper, we introduce the MoisesDB dataset for musical source\nseparation. It consists of 240 tracks from 45 artists, covering twelve musical\ngenres. For each song, we provide its individual audio sources, organized in a\ntwo-level hierarchical taxonomy of stems. This will facilitate building and\nevaluating fine-grained source separation systems that go beyond the limitation\nof using four stems (drums, bass, other, and vocals) due to lack of data. To\nfacilitate the adoption of this dataset, we publish an easy-to-use Python\nlibrary to download, process and use MoisesDB. Alongside a thorough\ndocumentation and analysis of the dataset contents, this work provides baseline\nresults for open-source separation models for varying separation granularities\n(four, five, and six stems), and discuss their results.\n", "date": "2023-07-29", "categories": []}
{"id": "2209.03143", "title": "AudioLM: a Language Modeling Approach to Audio Generation", "abstract": "  We introduce AudioLM, a framework for high-quality audio generation with\nlong-term consistency. AudioLM maps the input audio to a sequence of discrete\ntokens and casts audio generation as a language modeling task in this\nrepresentation space. We show how existing audio tokenizers provide different\ntrade-offs between reconstruction quality and long-term structure, and we\npropose a hybrid tokenization scheme to achieve both objectives. Namely, we\nleverage the discretized activations of a masked language model pre-trained on\naudio to capture long-term structure and the discrete codes produced by a\nneural audio codec to achieve high-quality synthesis. By training on large\ncorpora of raw audio waveforms, AudioLM learns to generate natural and coherent\ncontinuations given short prompts. When trained on speech, and without any\ntranscript or annotation, AudioLM generates syntactically and semantically\nplausible speech continuations while also maintaining speaker identity and\nprosody for unseen speakers. Furthermore, we demonstrate how our approach\nextends beyond speech by generating coherent piano music continuations, despite\nbeing trained without any symbolic representation of music.\n", "date": "2022-09-07", "categories": []}
{"id": "2307.12576", "title": "Self-refining of Pseudo Labels for Music Source Separation with Noisy\n  Labeled Data", "abstract": "  Music source separation (MSS) faces challenges due to the limited\navailability of correctly-labeled individual instrument tracks. With the push\nto acquire larger datasets to improve MSS performance, the inevitability of\nencountering mislabeled individual instrument tracks becomes a significant\nchallenge to address. This paper introduces an automated technique for refining\nthe labels in a partially mislabeled dataset. Our proposed self-refining\ntechnique, employed with a noisy-labeled dataset, results in only a 1% accuracy\ndegradation in multi-label instrument recognition compared to a classifier\ntrained on a clean-labeled dataset. The study demonstrates the importance of\nrefining noisy-labeled data in MSS model training and shows that utilizing the\nrefined dataset leads to comparable results derived from a clean-labeled\ndataset. Notably, upon only access to a noisy dataset, MSS models trained on a\nself-refined dataset even outperform those trained on a dataset refined with a\nclassifier trained on clean labels.\n", "date": "2023-07-24", "categories": []}
{"id": "2306.09382", "title": "Sound Demixing Challenge 2023 Music Demixing Track Technical Report:\n  TFC-TDF-UNet v3", "abstract": "  In this report, we present our award-winning solutions for the Music Demixing\nTrack of Sound Demixing Challenge 2023. First, we propose TFC-TDF-UNet v3, a\ntime-efficient music source separation model that achieves state-of-the-art\nresults on the MUSDB benchmark. We then give full details regarding our\nsolutions for each Leaderboard, including a loss masking approach for\nnoise-robust training. Code for reproducing model training and final\nsubmissions is available at github.com/kuielab/sdx23.\n", "date": "2023-06-15", "categories": []}
{"id": "2307.10994", "title": "Progressive distillation diffusion for raw music generation", "abstract": "  This paper aims to apply a new deep learning approach to the task of\ngenerating raw audio files. It is based on diffusion models, a recent type of\ndeep generative model. This new type of method has recently shown outstanding\nresults with image generation. A lot of focus has been given to those models by\nthe computer vision community. On the other hand, really few have been given\nfor other types of applications such as music generation in waveform domain.\n  In this paper the model for unconditional generating applied to music is\nimplemented: Progressive distillation diffusion with 1D U-Net. Then, a\ncomparison of different parameters of diffusion and their value in a full\nresult is presented. One big advantage of the methods implemented through this\nwork is the fact that the model is able to deal with progressing audio\nprocessing and generating , using transformation from 1-channel 128 x 384 to\n3-channel 128 x 128 mel-spectrograms and looped generation. The empirical\ncomparisons are realized across different self-collected datasets.\n", "date": "2023-07-20", "categories": []}
{"id": "2307.02909", "title": "Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation\n  and Recognition", "abstract": "  Accurate recognition of cocktail party speech containing overlapping\nspeakers, noise and reverberation remains a highly challenging task to date.\nMotivated by the invariance of visual modality to acoustic signal corruption,\nan audio-visual multi-channel speech separation, dereverberation and\nrecognition approach featuring a full incorporation of visual information into\nall system components is proposed in this paper. The efficacy of the video\ninput is consistently demonstrated in mask-based MVDR speech separation,\nDNN-WPE or spectral mapping (SpecM) based speech dereverberation front-end and\nConformer ASR back-end. Audio-visual integrated front-end architectures\nperforming speech separation and dereverberation in a pipelined or joint\nfashion via mask-based WPD are investigated. The error cost mismatch between\nthe speech enhancement front-end and ASR back-end components is minimized by\nend-to-end jointly fine-tuning using either the ASR cost function alone, or its\ninterpolation with the speech enhancement loss. Experiments were conducted on\nthe mixture overlapped and reverberant speech data constructed using simulation\nor replay of the Oxford LRS2 dataset. The proposed audio-visual multi-channel\nspeech separation, dereverberation and recognition systems consistently\noutperformed the comparable audio-only baseline by 9.1% and 6.2% absolute\n(41.7% and 36.0% relative) word error rate (WER) reductions. Consistent speech\nenhancement improvements were also obtained on PESQ, STOI and SRMR scores.\n", "date": "2023-07-06", "categories": []}
{"id": "2210.14321", "title": "Artificial ASMR: A Cyber-Psychological Approach", "abstract": "  The popularity of Autonomous Sensory Meridian Response (ASMR) has skyrockted\nover the past decade, but scientific studies on what exactly triggered ASMR\neffect remain few and immature, one most commonly acknowledged trigger is that\nASMR clips typically provide rich semantic information. With our attention\ncaught by the common acoustic patterns in ASMR audios, we investigate the\ncorrelation between the cyclic features of audio signals and their\neffectiveness in triggering ASMR effects. A cyber-psychological approach that\ncombines signal processing, artificial intelligence, and experimental\npsychology is taken, with which we are able to quantize ASMR-related acoustic\nfeatures, and therewith synthesize ASMR clips with random cyclic patterns but\nnot delivering identifiably scenarios to the audience, which were proven to be\neffective in triggering ASMR effects.\n", "date": "2022-10-25", "categories": []}
{"id": "2301.10752", "title": "Separate And Diffuse: Using a Pretrained Diffusion Model for Improving\n  Source Separation", "abstract": "  The problem of speech separation, also known as the cocktail party problem,\nrefers to the task of isolating a single speech signal from a mixture of speech\nsignals. Previous work on source separation derived an upper bound for the\nsource separation task in the domain of human speech. This bound is derived for\ndeterministic models. Recent advancements in generative models challenge this\nbound. We show how the upper bound can be generalized to the case of random\ngenerative models. Applying a diffusion model Vocoder that was pretrained to\nmodel single-speaker voices on the output of a deterministic separation model\nleads to state-of-the-art separation results. It is shown that this requires\none to combine the output of the separation model with that of the diffusion\nmodel. In our method, a linear combination is performed, in the frequency\ndomain, using weights that are inferred by a learned model. We show\nstate-of-the-art results on 2, 3, 5, 10, and 20 speakers on multiple\nbenchmarks. In particular, for two speakers, our method is able to surpass what\nwas previously considered the upper performance bound.\n", "date": "2023-01-25", "categories": []}
{"id": "2306.14191", "title": "PrimaDNN': A Characteristics-aware DNN Customization for Singing\n  Technique Detection", "abstract": "  Professional vocalists modulate their voice timbre or pitch to make their\nvocal performance more expressive. Such fluctuations are called singing\ntechniques. Automatic detection of singing techniques from audio tracks can be\nbeneficial to understand how each singer expresses the performance, yet it can\nalso be difficult due to the wide variety of the singing techniques. A deep\nneural network (DNN) model can handle such variety; however, there might be a\npossibility that considering the characteristics of the data improves the\nperformance of singing technique detection. In this paper, we propose PrimaDNN,\na CRNN model with a characteristics-oriented improvement. The features of the\nmodel are: 1) input feature representation based on auxiliary pitch information\nand multi-resolution mel spectrograms, 2) Convolution module based on the\nSqueeze-and-excitation (SENet) and the Instance normalization. In the results\nof J-POP singing technique detection, PrimaDNN achieved the best results of\n44.9% at the overall macro-F measure, compared to conventional works. We also\nfound that the contribution of each component varies depending on the type of\nsinging technique.\n", "date": "2023-06-25", "categories": []}
{"id": "2306.14647", "title": "Mono-to-stereo through parametric stereo generation", "abstract": "  Generating a stereophonic presentation from a monophonic audio signal is a\nchallenging open task, especially if the goal is to obtain a realistic spatial\nimaging with a specific panning of sound elements. In this work, we propose to\nconvert mono to stereo by means of predicting parametric stereo (PS) parameters\nusing both nearest neighbor and deep network approaches. In combination with\nPS, we also propose to model the task with generative approaches, allowing to\nsynthesize multiple and equally-plausible stereo renditions from the same mono\nsignal. To achieve this, we consider both autoregressive and masked token\nmodelling approaches. We provide evidence that the proposed PS-based models\noutperform a competitive classical decorrelation baseline and that, within a PS\nprediction framework, modern generative models outshine equivalent\nnon-generative counterparts. Overall, our work positions both PS and generative\nmodelling as strong and appealing methodologies for mono-to-stereo upmixing. A\ndiscussion of the limitations of these approaches is also provided.\n", "date": "2023-06-26", "categories": []}
{"id": "2306.12957", "title": "Siamese SIREN: Audio Compression with Implicit Neural Representations", "abstract": "  Implicit Neural Representations (INRs) have emerged as a promising method for\nrepresenting diverse data modalities, including 3D shapes, images, and audio.\nWhile recent research has demonstrated successful applications of INRs in image\nand 3D shape compression, their potential for audio compression remains largely\nunexplored. Motivated by this, we present a preliminary investigation into the\nuse of INRs for audio compression. Our study introduces Siamese SIREN, a novel\napproach based on the popular SIREN architecture. Our experimental results\nindicate that Siamese SIREN achieves superior audio reconstruction fidelity\nwhile utilizing fewer network parameters compared to previous INR\narchitectures.\n", "date": "2023-06-22", "categories": []}
{"id": "2303.05788", "title": "Deep Generative Fixed-filter Active Noise Control", "abstract": "  Due to the slow convergence and poor tracking ability, conventional LMS-based\nadaptive algorithms are less capable of handling dynamic noises. Selective\nfixed-filter active noise control (SFANC) can significantly reduce response\ntime by selecting appropriate pre-trained control filters for different noises.\nNonetheless, the limited number of pre-trained control filters may affect noise\nreduction performance, especially when the incoming noise differs much from the\ninitial noises during pre-training. Therefore, a generative fixed-filter active\nnoise control (GFANC) method is proposed in this paper to overcome the\nlimitation. Based on deep learning and a perfect-reconstruction filter bank,\nthe GFANC method only requires a few prior data (one pre-trained broadband\ncontrol filter) to automatically generate suitable control filters for various\nnoises. The efficacy of the GFANC method is demonstrated by numerical\nsimulations on real-recorded noises.\n", "date": "2023-03-10", "categories": []}
{"id": "2306.10240", "title": "Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source\n  Separation", "abstract": "  This paper describes an efficient unsupervised learning method for a neural\nsource separation model that utilizes a probabilistic generative model of\nobserved multichannel mixtures proposed for blind source separation (BSS). For\nthis purpose, amortized variational inference (AVI) has been used for directly\nsolving the inverse problem of BSS with full-rank spatial covariance analysis\n(FCA). Although this unsupervised technique called neural FCA is in principle\nfree from the domain mismatch problem, it is computationally demanding due to\nthe full rankness of the spatial model in exchange for robustness against\nrelatively short reverberations. To reduce the model complexity without\nsacrificing performance, we propose neural FastFCA based on the\njointly-diagonalizable yet full-rank spatial model. Our neural separation model\nintroduced for AVI alternately performs neural network blocks and single steps\nof an efficient iterative algorithm called iterative source steering. This\nalternating architecture enables the separation model to quickly separate the\nmixture spectrogram by leveraging both the deep neural network and the\nmultichannel optimization algorithm. The training objective with AVI is derived\nto maximize the marginalized likelihood of the observed mixtures. The\nexperiment using mixture signals of two to four sound sources shows that neural\nFastFCA outperforms conventional BSS methods and reduces the computational time\nto about 2% of that for the neural FCA.\n", "date": "2023-06-17", "categories": []}
{"id": "2306.10684", "title": "Visually-Guided Sound Source Separation with Audio-Visual Predictive\n  Coding", "abstract": "  The framework of visually-guided sound source separation generally consists\nof three parts: visual feature extraction, multimodal feature fusion, and sound\nsignal processing. An ongoing trend in this field has been to tailor involved\nvisual feature extractor for informative visual guidance and separately devise\nmodule for feature fusion, while utilizing U-Net by default for sound analysis.\nHowever, such divide-and-conquer paradigm is parameter inefficient and,\nmeanwhile, may obtain suboptimal performance as jointly optimizing and\nharmonizing various model components is challengeable. By contrast, this paper\npresents a novel approach, dubbed audio-visual predictive coding (AVPC), to\ntackle this task in a parameter efficient and more effective manner. The\nnetwork of AVPC features a simple ResNet-based video analysis network for\nderiving semantic visual features, and a predictive coding-based sound\nseparation network that can extract audio features, fuse multimodal\ninformation, and predict sound separation masks in the same architecture. By\niteratively minimizing the prediction error between features, AVPC integrates\naudio and visual information recursively, leading to progressively improved\nperformance. In addition, we develop a valid self-supervised learning strategy\nfor AVPC via co-predicting two audio-visual representations of the same sound\nsource. Extensive evaluations demonstrate that AVPC outperforms several\nbaselines in separating musical instrument sounds, while reducing the model\nsize significantly. Code is available at:\nhttps://github.com/zjsong/Audio-Visual-Predictive-Coding.\n", "date": "2023-06-19", "categories": []}
{"id": "2304.07142", "title": "On Data Sampling Strategies for Training Neural Network Speech\n  Separation Models", "abstract": "  Speech separation remains an important area of multi-speaker signal\nprocessing. Deep neural network (DNN) models have attained the best performance\non many speech separation benchmarks. Some of these models can take significant\ntime to train and have high memory requirements. Previous work has proposed\nshortening training examples to address these issues but the impact of this on\nmodel performance is not yet well understood. In this work, the impact of\napplying these training signal length (TSL) limits is analysed for two speech\nseparation models: SepFormer, a transformer model, and Conv-TasNet, a\nconvolutional model. The WJS0-2Mix, WHAMR and Libri2Mix datasets are analysed\nin terms of signal length distribution and its impact on training efficiency.\nIt is demonstrated that, for specific distributions, applying specific TSL\nlimits results in better performance. This is shown to be mainly due to\nrandomly sampling the start index of the waveforms resulting in more unique\nexamples for training. A SepFormer model trained using a TSL limit of 4.42s and\ndynamic mixing (DM) is shown to match the best-performing SepFormer model\ntrained with DM and unlimited signal lengths. Furthermore, the 4.42s TSL limit\nresults in a 44% reduction in training time with WHAMR.\n", "date": "2023-04-14", "categories": []}
{"id": "2306.07820", "title": "Unsupervised speech enhancement with deep dynamical generative speech\n  and noise models", "abstract": "  This work builds on a previous work on unsupervised speech enhancement using\na dynamical variational autoencoder (DVAE) as the clean speech model and\nnon-negative matrix factorization (NMF) as the noise model. We propose to\nreplace the NMF noise model with a deep dynamical generative model (DDGM)\ndepending either on the DVAE latent variables, or on the noisy observations, or\non both. This DDGM can be trained in three configurations: noise-agnostic,\nnoise-dependent and noise adaptation after noise-dependent training.\nExperimental results show that the proposed method achieves competitive\nperformance compared to state-of-the-art unsupervised speech enhancement\nmethods, while the noise-dependent training configuration yields a much more\ntime-efficient inference process.\n", "date": "2023-06-13", "categories": []}
{"id": "2306.05887", "title": "An Efficient Speech Separation Network Based on Recurrent Fusion Dilated\n  Convolution and Channel Attention", "abstract": "  We present an efficient speech separation neural network, ARFDCN, which\ncombines dilated convolutions, multi-scale fusion (MSF), and channel attention\nto overcome the limited receptive field of convolution-based networks and the\nhigh computational cost of transformer-based networks. The suggested network\narchitecture is encoder-decoder based. By using dilated convolutions with\ngradually increasing dilation value to learn local and global features and\nfusing them at adjacent stages, the model can learn rich feature content.\nMeanwhile, by adding channel attention modules to the network, the model can\nextract channel weights, learn more important features, and thus improve its\nexpressive power and robustness. Experimental results indicate that the model\nachieves a decent balance between performance and computational efficiency,\nmaking it a promising alternative to current mainstream models for practical\napplications.\n", "date": "2023-06-09", "categories": []}
{"id": "2306.04286", "title": "A Mask Free Neural Network for Monaural Speech Enhancement", "abstract": "  In speech enhancement, the lack of clear structural characteristics in the\ntarget speech phase requires the use of conservative and cumbersome network\nframeworks. It seems difficult to achieve competitive performance using direct\nmethods and simple network architectures. However, we propose the MFNet, a\ndirect and simple network that can not only map speech but also map reverse\nnoise. This network is constructed by stacking global local former blocks\n(GLFBs), which combine the advantages of Mobileblock for global processing and\nMetaformer architecture for local interaction. Our experimental results\ndemonstrate that our network using mapping method outperforms masking methods,\nand direct mapping of reverse noise is the optimal solution in strong noise\nenvironments. In a horizontal comparison on the 2020 Deep Noise Suppression\n(DNS) challenge test set without reverberation, to the best of our knowledge,\nMFNet is the current state-of-the-art (SOTA) mapping model.\n", "date": "2023-06-07", "categories": []}
{"id": "2306.03177", "title": "DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic\n  Echo Cancellation, Noise Suppression and Dereverberation", "abstract": "  Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation\n(DR) are an integral part of modern full-duplex communication systems. As the\ndemand for teleconferencing systems increases, addressing these tasks is\nrequired for an effective and efficient online meeting experience. Most prior\nresearch proposes solutions for these tasks separately, combining them with\ndigital signal processing (DSP) based components, resulting in complex\npipelines that are often impractical to deploy in real-world applications. This\npaper proposes a real-time cross-attention deep model, named DeepVQE, based on\nresidual convolutional neural networks (CNNs) and recurrent neural networks\n(RNNs) to simultaneously address AEC, NS, and DR. We conduct several ablation\nstudies to analyze the contributions of different components of our model to\nthe overall performance. DeepVQE achieves state-of-the-art performance on\nnon-personalized tracks from the ICASSP 2023 Acoustic Echo Cancellation\nChallenge and ICASSP 2023 Deep Noise Suppression Challenge test sets, showing\nthat a single model can handle multiple tasks with excellent performance.\nMoreover, the model runs in real-time and has been successfully tested for the\nMicrosoft Teams platform.\n", "date": "2023-06-05", "categories": []}
{"id": "2210.14536", "title": "Position tracking of a varying number of sound sources with sliding\n  permutation invariant training", "abstract": "  Recent data- and learning-based sound source localization (SSL) methods have\nshown strong performance in challenging acoustic scenarios. However, little\nwork has been done on adapting such methods to track consistently multiple\nsources appearing and disappearing, as would occur in reality. In this paper,\nwe present a new training strategy for deep learning SSL models with a\nstraightforward implementation based on the mean squared error of the optimal\nassociation between estimated and reference positions in the preceding time\nframes. It optimizes the desired properties of a tracking system: handling a\ntime-varying number of sources and ordering localization estimates according to\ntheir trajectories, minimizing identity switches (IDSs). Evaluation on\nsimulated data of multiple reverberant moving sources and on two model\narchitectures proves its effectiveness on reducing identity switches without\ncompromising frame-wise localization accuracy.\n", "date": "2022-10-26", "categories": []}
{"id": "2211.04332", "title": "DiffPhase: Generative Diffusion-based STFT Phase Retrieval", "abstract": "  Diffusion probabilistic models have been recently used in a variety of tasks,\nincluding speech enhancement and synthesis. As a generative approach, diffusion\nmodels have been shown to be especially suitable for imputation problems, where\nmissing data is generated based on existing data. Phase retrieval is inherently\nan imputation problem, where phase information has to be generated based on the\ngiven magnitude. In this work we build upon previous work in the speech domain,\nadapting a speech enhancement diffusion model specifically for STFT phase\nretrieval. Evaluation using speech quality and intelligibility metrics shows\nthe diffusion approach is well-suited to the phase retrieval task, with\nperformance surpassing both classical and modern methods.\n", "date": "2022-11-08", "categories": []}
{"id": "2306.01332", "title": "Differentiable Grey-box Modelling of Phaser Effects using Frame-based\n  Spectral Processing", "abstract": "  Machine learning approaches to modelling analog audio effects have seen\nintensive investigation in recent years, particularly in the context of\nnon-linear time-invariant effects such as guitar amplifiers. For modulation\neffects such as phasers, however, new challenges emerge due to the presence of\nthe low-frequency oscillator which controls the slowly time-varying nature of\nthe effect. Existing approaches have either required foreknowledge of this\ncontrol signal, or have been non-causal in implementation. This work presents a\ndifferentiable digital signal processing approach to modelling phaser effects\nin which the underlying control signal and time-varying spectral response of\nthe effect are jointly learned. The proposed model processes audio in short\nframes to implement a time-varying filter in the frequency domain, with a\ntransfer function based on typical analog phaser circuit topology. We show that\nthe model can be trained to emulate an analog reference device, while retaining\ninterpretable and adjustable parameters. The frame duration is an important\nhyper-parameter of the proposed model, so an investigation was carried out into\nits effect on model accuracy. The optimal frame length depends on both the rate\nand transient decay-time of the target effect, but the frame length can be\naltered at inference time without a significant change in accuracy.\n", "date": "2023-06-02", "categories": []}
{"id": "2306.00160", "title": "Audio-Visual Speech Separation in Noisy Environments with a Lightweight\n  Iterative Model", "abstract": "  We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and\nlightweight neural network that uses Progressive Learning (PL) to perform\naudio-visual speech separation in noisy environments. To this end, we adopt the\nAsynchronous Fully Recurrent Convolutional Neural Network (A-FRCNN), which has\nshown successful results in audio-only speech separation. Our architecture\nconsists of an audio branch and a video branch, with iterative A-FRCNN blocks\nsharing weights for each modality. We evaluated our model in a controlled\nenvironment using the NTCD-TIMIT dataset and in-the-wild using a synthetic\ndataset that combines LRS3 and WHAM!. The experiments demonstrate the\nsuperiority of our model in both settings with respect to various audio-only\nand audio-visual baselines. Furthermore, the reduced footprint of our model\nmakes it suitable for low resource applications.\n", "date": "2023-05-31", "categories": []}
{"id": "2211.02940", "title": "Effective Audio Classification Network Based on Paired Inverse Pyramid\n  Structure and Dense MLP Block", "abstract": "  Recently, massive architectures based on Convolutional Neural Network (CNN)\nand self-attention mechanisms have become necessary for audio classification.\nWhile these techniques are state-of-the-art, these works' effectiveness can\nonly be guaranteed with huge computational costs and parameters, large amounts\nof data augmentation, transfer from large datasets and some other tricks. By\nutilizing the lightweight nature of audio, we propose an efficient network\nstructure called Paired Inverse Pyramid Structure (PIP) and a network called\nPaired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\\% of\nEnvironmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset\nand 93.2\\% of Music Genre Classification (MGC) on the GTAZN dataset, with only\n1 million parameters. Both of the results are achieved without data\naugmentation or model transfer. Public code is available at:\nhttps://github.com/JNAIC/PIPMN\n", "date": "2022-11-05", "categories": []}
{"id": "2303.00529", "title": "Extending DNN-based Multiplicative Masking to Deep Subband Filtering for\n  Improved Dereverberation", "abstract": "  In this paper, we present a scheme for extending deep neural network-based\nmultiplicative maskers to deep subband filters for speech restoration in the\ntime-frequency domain. The resulting method can be generically applied to any\ndeep neural network providing masks in the time-frequency domain, while\nrequiring only few more trainable parameters and a computational overhead that\nis negligible for state-of-the-art neural networks. We demonstrate that the\nresulting deep subband filtering scheme outperforms multiplicative masking for\ndereverberation, while leaving the denoising performance virtually the same. We\nargue that this is because deep subband filtering in the time-frequency domain\nfits the subband approximation often assumed in the dereverberation literature,\nwhereas multiplicative masking corresponds to the narrowband approximation\ngenerally employed for denoising.\n", "date": "2023-03-01", "categories": []}
{"id": "2305.19458", "title": "A Unified Audio-Visual Learning Framework for Localization, Separation,\n  and Recognition", "abstract": "  The ability to accurately recognize, localize and separate sound sources is\nfundamental to any audio-visual perception task. Historically, these abilities\nwere tackled separately, with several methods developed independently for each\ntask. However, given the interconnected nature of source localization,\nseparation, and recognition, independent models are likely to yield suboptimal\nperformance as they fail to capture the interdependence between these tasks. To\naddress this problem, we propose a unified audio-visual learning framework\n(dubbed OneAVM) that integrates audio and visual cues for joint localization,\nseparation, and recognition. OneAVM comprises a shared audio-visual encoder and\ntask-specific decoders trained with three objectives. The first objective\naligns audio and visual representations through a localized audio-visual\ncorrespondence loss. The second tackles visual source separation using a\ntraditional mix-and-separate framework. Finally, the third objective reinforces\nvisual feature separation and localization by mixing images in pixel space and\naligning their representations with those of all corresponding sound sources.\nExtensive experiments on MUSIC, VGG-Instruments, VGG-Music, and VGGSound\ndatasets demonstrate the effectiveness of OneAVM for all three tasks,\naudio-visual source localization, separation, and nearest neighbor recognition,\nand empirically demonstrate a strong positive transfer between them.\n", "date": "2023-05-30", "categories": []}
{"id": "2302.14748", "title": "Reducing the Prior Mismatch of Stochastic Differential Equations for\n  Diffusion-based Speech Enhancement", "abstract": "  Recently, score-based generative models have been successfully employed for\nthe task of speech enhancement. A stochastic differential equation is used to\nmodel the iterative forward process, where at each step environmental noise and\nwhite Gaussian noise are added to the clean speech signal. While in limit the\nmean of the forward process ends at the noisy mixture, in practice it stops\nearlier and thus only at an approximation of the noisy mixture. This results in\na discrepancy between the terminating distribution of the forward process and\nthe prior used for solving the reverse process at inference. In this paper, we\naddress this discrepancy and propose a forward process based on a Brownian\nbridge. We show that such a process leads to a reduction of the mismatch\ncompared to previous diffusion processes. More importantly, we show that our\napproach improves in objective metrics over the baseline process with only half\nof the iteration steps and having one hyperparameter less to tune.\n", "date": "2023-02-28", "categories": []}
{"id": "2305.16862", "title": "Neural modeling of magnetic tape recorders", "abstract": "  The sound of magnetic recording media, such as open-reel and cassette tape\nrecorders, is still sought after by today's sound practitioners due to the\nimperfections embedded in the physics of the magnetic recording process. This\npaper proposes a method for digitally emulating this character using neural\nnetworks. The signal chain of the proposed system consists of three main\ncomponents: the hysteretic nonlinearity and filtering jointly produced by the\nmagnetic recording process as well as the record and playback amplifiers, the\nfluctuating delay originating from the tape transport, and the combined\nadditive noise component from various electromagnetic origins. In our approach,\nthe hysteretic nonlinear block is modeled using a recurrent neural network,\nwhile the delay trajectories and the noise component are generated using\nseparate diffusion models, which employ U-net deep convolutional neural\nnetworks. According to the conducted objective evaluation, the proposed\narchitecture faithfully captures the character of the magnetic tape recorder.\nThe results of this study can be used to construct virtual replicas of vintage\nsound recording devices with applications in music production and audio\nantiquing tasks.\n", "date": "2023-05-26", "categories": []}
{"id": "2305.16932", "title": "A Neural State-Space Model Approach to Efficient Speech Separation", "abstract": "  In this work, we introduce S4M, a new efficient speech separation framework\nbased on neural state-space models (SSM). Motivated by linear time-invariant\nsystems for sequence modeling, our SSM-based approach can efficiently model\ninput signals into a format of linear ordinary differential equations (ODEs)\nfor representation learning. To extend the SSM technique into speech separation\ntasks, we first decompose the input mixture into multi-scale representations\nwith different resolutions. This mechanism enables S4M to learn globally\ncoherent separation and reconstruction. The experimental results show that S4M\nperforms comparably to other separation backbones in terms of SI-SDRi, while\nhaving a much lower model complexity with significantly fewer trainable\nparameters. In addition, our S4M-tiny model (1.8M parameters) even surpasses\nattention-based Sepformer (26.0M parameters) in noisy conditions with only 9.2\nof multiply-accumulate operation (MACs).\n", "date": "2023-05-26", "categories": []}
{"id": "2305.13262", "title": "Modulation Extraction for LFO-driven Audio Effects", "abstract": "  Low frequency oscillator (LFO) driven audio effects such as phaser, flanger,\nand chorus, modify an input signal using time-varying filters and delays,\nresulting in characteristic sweeping or widening effects. It has been shown\nthat these effects can be modeled using neural networks when conditioned with\nthe ground truth LFO signal. However, in most cases, the LFO signal is not\naccessible and measurement from the audio signal is nontrivial, hindering the\nmodeling process. To address this, we propose a framework capable of extracting\narbitrary LFO signals from processed audio across multiple digital audio\neffects, parameter settings, and instrument configurations. Since our system\nimposes no restrictions on the LFO signal shape, we demonstrate its ability to\nextract quasiperiodic, combined, and distorted modulation signals that are\nrelevant to effect modeling. Furthermore, we show how coupling the extraction\nmodel with a simple processing network enables training of end-to-end black-box\nmodels of unseen analog or digital LFO-driven audio effects using only dry and\nwet audio pairs, overcoming the need to access the audio effect or internal LFO\nsignal. We make our code available and provide the trained audio effect models\nin a real-time VST plugin.\n", "date": "2023-05-22", "categories": []}
{"id": "2305.09994", "title": "BASEN: Time-Domain Brain-Assisted Speech Enhancement Network with\n  Convolutional Cross Attention in Multi-talker Conditions", "abstract": "  Time-domain single-channel speech enhancement (SE) still remains challenging\nto extract the target speaker without any prior information on multi-talker\nconditions. It has been shown via auditory attention decoding that the brain\nactivity of the listener contains the auditory information of the attended\nspeaker. In this paper, we thus propose a novel time-domain brain-assisted SE\nnetwork (BASEN) incorporating electroencephalography (EEG) signals recorded\nfrom the listener for extracting the target speaker from monaural speech\nmixtures. The proposed BASEN is based on the fully-convolutional time-domain\naudio separation network. In order to fully leverage the complementary\ninformation contained in the EEG signals, we further propose a convolutional\nmulti-layer cross attention module to fuse the dual-branch features.\nExperimental results on a public dataset show that the proposed model\noutperforms the state-of-the-art method in several evaluation metrics. The\nreproducible code is available at https://github.com/jzhangU/Basen.git.\n", "date": "2023-05-17", "categories": []}
{"id": "2212.04831", "title": "Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian\n  Mixture Models", "abstract": "  Single-channel deep speech enhancement approaches often estimate a single\nmultiplicative mask to extract clean speech without a measure of its accuracy.\nInstead, in this work, we propose to quantify the uncertainty associated with\nclean speech estimates in neural network-based speech enhancement. Predictive\nuncertainty is typically categorized into aleatoric uncertainty and epistemic\nuncertainty. The former accounts for the inherent uncertainty in data and the\nlatter corresponds to the model uncertainty. Aiming for robust clean speech\nestimation and efficient predictive uncertainty quantification, we propose to\nintegrate statistical complex Gaussian mixture models (CGMMs) into a deep\nspeech enhancement framework. More specifically, we model the dependency\nbetween input and output stochastically by means of a conditional probability\ndensity and train a neural network to map the noisy input to the full posterior\ndistribution of clean speech, modeled as a mixture of multiple complex Gaussian\ncomponents. Experimental results on different datasets show that the proposed\nalgorithm effectively captures predictive uncertainty and that combining\npowerful statistical models and deep learning also delivers a superior speech\nenhancement performance.\n", "date": "2022-12-09", "categories": []}
{"id": "2305.06640", "title": "Speaker Diaphragm Excursion Prediction: deep attention and online\n  adaptation", "abstract": "  Speaker protection algorithm is to leverage the playback signal properties to\nprevent over excursion while maintaining maximum loudness, especially for the\nmobile phone with tiny loudspeakers. This paper proposes efficient DL solutions\nto accurately model and predict the nonlinear excursion, which is challenging\nfor conventional solutions. Firstly, we build the experiment and pre-processing\npipeline, where the feedback current and voltage are sampled as input, and\nlaser is employed to measure the excursion as ground truth. Secondly, one\nFFTNet model is proposed to explore the dominant low-frequency and other\nunknown harmonics, and compares to a baseline ConvNet model. In addition, BN\nre-estimation is designed to explore the online adaptation; and INT8\nquantization based on AI Model efficiency toolkit (AIMET\\footnote{AIMET is a\nproduct of Qualcomm Innovation Center, Inc.}) is applied to further reduce the\ncomplexity. The proposed algorithm is verified in two speakers and 3 typical\ndeployment scenarios, and $>$99\\% residual DC is less than 0.1 mm, much better\nthan traditional solutions.\n", "date": "2023-05-11", "categories": []}
{"id": "2211.00497", "title": "Modelling black-box audio effects with time-varying feature modulation", "abstract": "  Deep learning approaches for black-box modelling of audio effects have shown\npromise, however, the majority of existing work focuses on nonlinear effects\nwith behaviour on relatively short time-scales, such as guitar amplifiers and\ndistortion. While recurrent and convolutional architectures can theoretically\nbe extended to capture behaviour at longer time scales, we show that simply\nscaling the width, depth, or dilation factor of existing architectures does not\nresult in satisfactory performance when modelling audio effects such as fuzz\nand dynamic range compression. To address this, we propose the integration of\ntime-varying feature-wise linear modulation into existing temporal\nconvolutional backbones, an approach that enables learnable adaptation of the\nintermediate activations. We demonstrate that our approach more accurately\ncaptures long-range dependencies for a range of fuzz and compressor\nimplementations across both time and frequency domain metrics. We provide sound\nexamples, source code, and pretrained models to faciliate reproducibility.\n", "date": "2022-11-01", "categories": []}
{"id": "2305.05780", "title": "Enhancing Gappy Speech Audio Signals with Generative Adversarial\n  Networks", "abstract": "  Gaps, dropouts and short clips of corrupted audio are a common problem and\nparticularly annoying when they occur in speech. This paper uses machine\nlearning to regenerate gaps of up to 320ms in an audio speech signal. Audio\nregeneration is translated into image regeneration by transforming audio into a\nMel-spectrogram and using image in-painting to regenerate the gaps. The full\nMel-spectrogram is then transferred back to audio using the Parallel-WaveGAN\nvocoder and integrated into the audio stream. Using a sample of 1300 spoken\naudio clips of between 1 and 10 seconds taken from the publicly-available\nLJSpeech dataset our results show regeneration of audio gaps in close to real\ntime using GANs with a GPU equipped system. As expected, the smaller the gap in\nthe audio, the better the quality of the filled gaps. On a gap of 240ms the\naverage mean opinion score (MOS) for the best performing models was 3.737, on a\nscale of 1 (worst) to 5 (best) which is sufficient for a human to perceive as\nclose to uninterrupted human speech.\n", "date": "2023-05-09", "categories": []}
{"id": "2305.05591", "title": "AudioSlots: A slot-centric generative model for audio separation", "abstract": "  In a range of recent works, object-centric architectures have been shown to\nbe suitable for unsupervised scene decomposition in the vision domain. Inspired\nby these methods we present AudioSlots, a slot-centric generative model for\nblind source separation in the audio domain. AudioSlots is built using\npermutation-equivariant encoder and decoder networks. The encoder network based\non the Transformer architecture learns to map a mixed audio spectrogram to an\nunordered set of independent source embeddings. The spatial broadcast decoder\nnetwork learns to generate the source spectrograms from the source embeddings.\nWe train the model in an end-to-end manner using a permutation invariant loss\nfunction. Our results on Libri2Mix speech separation constitute a proof of\nconcept that this approach shows promise. We discuss the results and\nlimitations of our approach in detail, and further outline potential ways to\novercome the limitations and directions for future work.\n", "date": "2023-05-09", "categories": []}
{"id": "2211.07302", "title": "MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation", "abstract": "  Separation of multiple singing voices into each voice is a rarely studied\narea in music source separation research. The absence of a benchmark dataset\nhas hindered its progress. In this paper, we present an evaluation dataset and\nprovide baseline studies for multiple singing voices separation. First, we\nintroduce MedleyVox, an evaluation dataset for multiple singing voices\nseparation. We specify the problem definition in this dataset by categorizing\nit into i) unison, ii) duet, iii) main vs. rest, and iv) N-singing separation.\nSecond, to overcome the absence of existing multi-singing datasets for a\ntraining purpose, we present a strategy for construction of multiple singing\nmixtures using various single-singing datasets. Third, we propose the improved\nsuper-resolution network (iSRNet), which greatly enhances initial estimates of\nseparation networks. Jointly trained with the Conv-TasNet and the multi-singing\nmixture construction strategy, the proposed iSRNet achieved comparable\nperformance to ideal time-frequency masks on duet and unison subsets of\nMedleyVox. Audio samples, the dataset, and codes are available on our website\n(https://github.com/jeonchangbin49/MedleyVox).\n", "date": "2022-11-14", "categories": []}
{"id": "2305.01758", "title": "Adversarial Generative NMF for Single Channel Source Separation", "abstract": "  The idea of adversarial learning of regularization functionals has recently\nbeen introduced in the wider context of inverse problems. The intuition behind\nthis method is the realization that it is not only necessary to learn the basic\nfeatures that make up a class of signals one wants to represent, but also, or\neven more so, which features to avoid in the representation. In this paper, we\nwill apply this approach to the problem of source separation by means of\nnon-negative matrix factorization (NMF) and present a new method for the\nadversarial training of NMF bases. We show in numerical experiments, both for\nimage and audio separation, that this leads to a clear improvement of the\nreconstructed signals, in particular in the case where little or no strong\nsupervision data is available.\n", "date": "2023-04-24", "categories": []}
{"id": "2211.00878", "title": "Neural Fourier Shift for Binaural Speech Rendering", "abstract": "  We present a neural network for rendering binaural speech from given monaural\naudio, position, and orientation of the source. Most of the previous works have\nfocused on synthesizing binaural speeches by conditioning the positions and\norientations in the feature space of convolutional neural networks. These\nsynthesis approaches are powerful in estimating the target binaural speeches\neven for in-the-wild data but are difficult to generalize for rendering the\naudio from out-of-distribution domains. To alleviate this, we propose Neural\nFourier Shift (NFS), a novel network architecture that enables binaural speech\nrendering in the Fourier space. Specifically, utilizing a geometric time delay\nbased on the distance between the source and the receiver, NFS is trained to\npredict the delays and scales of various early reflections. NFS is efficient in\nboth memory and computational cost, is interpretable, and operates\nindependently of the source domain by its design. Experimental results show\nthat NFS performs comparable to the previous studies on the benchmark dataset,\neven with its 25 times lighter memory and 6 times fewer calculations.\n", "date": "2022-11-02", "categories": []}
{"id": "2304.11708", "title": "Sound-based drone fault classification using multitask learning", "abstract": "  The drone has been used for various purposes, including military\napplications, aerial photography, and pesticide spraying. However, the drone is\nvulnerable to external disturbances, and malfunction in propellers and motors\ncan easily occur. To improve the safety of drone operations, one should detect\nthe mechanical faults of drones in real-time. This paper proposes a sound-based\ndeep neural network (DNN) fault classifier and drone sound dataset. The dataset\nwas constructed by collecting the operating sounds of drones from microphones\nmounted on three different drones in an anechoic chamber. The dataset includes\nvarious operating conditions of drones, such as flight directions (front, back,\nright, left, clockwise, counterclockwise) and faults on propellers and motors.\nThe drone sounds were then mixed with noises recorded in five different spots\non the university campus, with a signal-to-noise ratio (SNR) varying from 10 dB\nto 15 dB. Using the acquired dataset, we train a DNN classifier, 1DCNN-ResNet,\nthat classifies the types of mechanical faults and their locations from\nshort-time input waveforms. We employ multitask learning (MTL) and incorporate\nthe direction classification task as an auxiliary task to make the classifier\nlearn more general audio features. The test over unseen data reveals that the\nproposed multitask model can successfully classify faults in drones and\noutperforms single-task models even with less training data.\n", "date": "2023-04-23", "categories": []}
{"id": "2211.02250", "title": "Real-Time Target Sound Extraction", "abstract": "  We present the first neural network model to achieve real-time and streaming\ntarget sound extraction. To accomplish this, we propose Waveformer, an\nencoder-decoder architecture with a stack of dilated causal convolution layers\nas the encoder, and a transformer decoder layer as the decoder. This hybrid\narchitecture uses dilated causal convolutions for processing large receptive\nfields in a computationally efficient manner while also leveraging the\ngeneralization performance of transformer-based architectures. Our evaluations\nshow as much as 2.2-3.3 dB improvement in SI-SNRi compared to the prior models\nfor this task while having a 1.2-4x smaller model size and a 1.5-2x lower\nruntime. We provide code, dataset, and audio samples:\nhttps://waveformer.cs.washington.edu/.\n", "date": "2022-11-04", "categories": []}
{"id": "2210.04222", "title": "Correlative Information Maximization Based Biologically Plausible Neural\n  Networks for Correlated Source Separation", "abstract": "  The brain effortlessly extracts latent causes of stimuli, but how it does\nthis at the network level remains unknown. Most prior attempts at this problem\nproposed neural networks that implement independent component analysis which\nworks under the limitation that latent causes are mutually independent. Here,\nwe relax this limitation and propose a biologically plausible neural network\nthat extracts correlated latent sources by exploiting information about their\ndomains. To derive this network, we choose maximum correlative information\ntransfer from inputs to outputs as the separation objective under the\nconstraint that the outputs are restricted to their presumed sets. The online\nformulation of this optimization problem naturally leads to neural networks\nwith local learning rules. Our framework incorporates infinitely many source\ndomain choices and flexibly models complex latent structures. Choices of\nsimplex or polytopic source domains result in networks with piecewise-linear\nactivation functions. We provide numerical examples to demonstrate the superior\ncorrelated source separation capability for both synthetic and natural sources.\n", "date": "2022-10-09", "categories": []}
{"id": "2304.02714", "title": "Learning Stage-wise GANs for Whistle Extraction in Time-Frequency\n  Spectrograms", "abstract": "  Whistle contour extraction aims to derive animal whistles from time-frequency\nspectrograms as polylines. For toothed whales, whistle extraction results can\nserve as the basis for analyzing animal abundance, species identity, and social\nactivities. During the last few decades, as long-term recording systems have\nbecome affordable, automated whistle extraction algorithms were proposed to\nprocess large volumes of recording data. Recently, a deep learning-based method\ndemonstrated superior performance in extracting whistles under varying noise\nconditions. However, training such networks requires a large amount of\nlabor-intensive annotation, which is not available for many species. To\novercome this limitation, we present a framework of stage-wise generative\nadversarial networks (GANs), which compile new whistle data suitable for deep\nmodel training via three stages: generation of background noise in the\nspectrogram, generation of whistle contours, and generation of whistle signals.\nBy separating the generation of different components in the samples, our\nframework composes visually promising whistle data and labels even when few\nexpert annotated data are available. Regardless of the amount of\nhuman-annotated data, the proposed data augmentation framework leads to a\nconsistent improvement in performance of the whistle extraction model, with a\nmaximum increase of 1.69 in the whistle extraction mean F1-score. Our\nstage-wise GAN also surpasses one single GAN in improving whistle extraction\nmodels with augmented data. The data and code will be available at\nhttps://github.com/Paul-LiPu/CompositeGAN\\_WhistleAugment.\n", "date": "2023-04-05", "categories": []}
{"id": "2304.02160", "title": "Pac-HuBERT: Self-Supervised Music Source Separation via Primitive\n  Auditory Clustering and Hidden-Unit BERT", "abstract": "  In spite of the progress in music source separation research, the small\namount of publicly-available clean source data remains a constant limiting\nfactor for performance. Thus, recent advances in self-supervised learning\npresent a largely-unexplored opportunity for improving separation models by\nleveraging unlabelled music data. In this paper, we propose a self-supervised\nlearning framework for music source separation inspired by the HuBERT speech\nrepresentation model. We first investigate the potential impact of the original\nHuBERT model by inserting an adapted version of it into the well-known Demucs\nV2 time-domain separation model architecture. We then propose a\ntime-frequency-domain self-supervised model, Pac-HuBERT (for primitive auditory\nclustering HuBERT), that we later use in combination with a Res-U-Net decoder\nfor source separation. Pac-HuBERT uses primitive auditory features of music as\nunsupervised clustering labels to initialize the self-supervised pretraining\nprocess using the Free Music Archive (FMA) dataset. The resulting framework\nachieves better source-to-distortion ratio (SDR) performance on the MusDB18\ntest set than the original Demucs V2 and Res-U-Net models. We further\ndemonstrate that it can boost performance with small amounts of supervised\ndata. Ultimately, our proposed framework is an effective solution to the\nchallenge of limited clean source data for music source separation.\n", "date": "2023-04-04", "categories": []}
{"id": "2211.02420", "title": "Spatially Selective Deep Non-linear Filters for Speaker Extraction", "abstract": "  In a scenario with multiple persons talking simultaneously, the spatial\ncharacteristics of the signals are the most distinct feature for extracting the\ntarget signal. In this work, we develop a deep joint spatial-spectral\nnon-linear filter that can be steered in an arbitrary target direction. For\nthis we propose a simple and effective conditioning mechanism, which sets the\ninitial state of the filter's recurrent layers based on the target direction.\nWe show that this scheme is more effective than the baseline approach and\nincreases the flexibility of the filter at no performance cost. The resulting\nspatially selective non-linear filters can also be used for speech separation\nof an arbitrary number of speakers and enable very accurate multi-speaker\nlocalization as we demonstrate in this paper.\n", "date": "2022-11-04", "categories": []}
{"id": "2209.15200", "title": "An efficient encoder-decoder architecture with top-down attention for\n  speech separation", "abstract": "  Deep neural networks have shown excellent prospects in speech separation\ntasks. However, obtaining good results while keeping a low model complexity\nremains challenging in real-world applications. In this paper, we provide a\nbio-inspired efficient encoder-decoder architecture by mimicking the brain's\ntop-down attention, called TDANet, with decreased model complexity without\nsacrificing performance. The top-down attention in TDANet is extracted by the\nglobal attention (GA) module and the cascaded local attention (LA) layers. The\nGA module takes multi-scale acoustic features as input to extract global\nattention signal, which then modulates features of different scales by direct\ntop-down connections. The LA layers use features of adjacent layers as input to\nextract the local attention signal, which is used to modulate the lateral input\nin a top-down manner. On three benchmark datasets, TDANet consistently achieved\ncompetitive separation performance to previous state-of-the-art (SOTA) methods\nwith higher efficiency. Specifically, TDANet's multiply-accumulate operations\n(MACs) are only 5\\% of Sepformer, one of the previous SOTA models, and CPU\ninference time is only 10\\% of Sepformer. In addition, a large-size version of\nTDANet obtained SOTA results on three datasets, with MACs still only 10\\% of\nSepformer and the CPU inference time only 24\\% of Sepformer.\n", "date": "2022-09-30", "categories": []}
{"id": "2303.15042", "title": "Partially Adaptive Multichannel Joint Reduction of Ego-noise and\n  Environmental Noise", "abstract": "  Human-robot interaction relies on a noise-robust audio processing module\ncapable of estimating target speech from audio recordings impacted by\nenvironmental noise, as well as self-induced noise, so-called ego-noise. While\nexternal ambient noise sources vary from environment to environment, ego-noise\nis mainly caused by the internal motors and joints of a robot. Ego-noise and\nenvironmental noise reduction are often decoupled, i.e., ego-noise reduction is\nperformed without considering environmental noise. Recently, a variational\nautoencoder (VAE)-based speech model has been combined with a fully adaptive\nnon-negative matrix factorization (NMF) noise model to recover clean speech\nunder different environmental noise disturbances. However, its enhancement\nperformance is limited in adverse acoustic scenarios involving, e.g. ego-noise.\nIn this paper, we propose a multichannel partially adaptive scheme to jointly\nmodel ego-noise and environmental noise utilizing the VAE-NMF framework, where\nwe take advantage of spatially and spectrally structured characteristics of\nego-noise by pre-training the ego-noise model, while retaining the ability to\nadapt to unknown environmental noise. Experimental results show that our\nproposed approach outperforms the methods based on a completely fixed scheme\nand a fully adaptive scheme when ego-noise and environmental noise are present\nsimultaneously.\n", "date": "2023-03-27", "categories": []}
{"id": "2303.07449", "title": "Blind Acoustic Room Parameter Estimation Using Phase Features", "abstract": "  Modeling room acoustics in a field setting involves some degree of blind\nparameter estimation from noisy and reverberant audio. Modern approaches\nleverage convolutional neural networks (CNNs) in tandem with time-frequency\nrepresentation. Using short-time Fourier transforms to develop these\nspectrogram-like features has shown promising results, but this method\nimplicitly discards a significant amount of audio information in the phase\ndomain. Inspired by recent works in speech enhancement, we propose utilizing\nnovel phase-related features to extend recent approaches to blindly estimate\nthe so-called \"reverberation fingerprint\" parameters, namely, volume and RT60.\nThe addition of these features is shown to outperform existing methods that\nrely solely on magnitude-based spectral features across a wide range of\nacoustics spaces. We evaluate the effectiveness of the deployment of these\nnovel features in both single-parameter and multi-parameter estimation\nstrategies, using a novel dataset that consists of publicly available room\nimpulse responses (RIRs), synthesized RIRs, and in-house measurements of real\nacoustic spaces.\n", "date": "2023-03-13", "categories": []}
{"id": "2208.10325", "title": "Exploiting Temporal Structures of Cyclostationary Signals for\n  Data-Driven Single-Channel Source Separation", "abstract": "  We study the problem of single-channel source separation (SCSS), and focus on\ncyclostationary signals, which are particularly suitable in a variety of\napplication domains. Unlike classical SCSS approaches, we consider a setting\nwhere only examples of the sources are available rather than their models,\ninspiring a data-driven approach. For source models with underlying\ncyclostationary Gaussian constituents, we establish a lower bound on the\nattainable mean squared error (MSE) for any separation method, model-based or\ndata-driven. Our analysis further reveals the operation for optimal separation\nand the associated implementation challenges. As a computationally attractive\nalternative, we propose a deep learning approach using a U-Net architecture,\nwhich is competitive with the minimum MSE estimator. We demonstrate in\nsimulation that, with suitable domain-informed architectural choices, our U-Net\nmethod can approach the optimal performance with substantially reduced\ncomputational burden.\n", "date": "2022-08-22", "categories": []}
{"id": "2211.12590", "title": "Deep Neural Mel-Subband Beamformer for In-car Speech Separation", "abstract": "  While current deep learning (DL)-based beamforming techniques have been\nproved effective in speech separation, they are often designed to process\nnarrow-band (NB) frequencies independently which results in higher\ncomputational costs and inference times, making them unsuitable for real-world\nuse. In this paper, we propose DL-based mel-subband spatio-temporal beamformer\nto perform speech separation in a car environment with reduced computation cost\nand inference time. As opposed to conventional subband (SB) approaches, our\nframework uses a mel-scale based subband selection strategy which ensures a\nfine-grained processing for lower frequencies where most speech formant\nstructure is present, and coarse-grained processing for higher frequencies. In\na recursive way, robust frame-level beamforming weights are determined for each\nspeaker location/zone in a car from the estimated subband speech and noise\ncovariance matrices. Furthermore, proposed framework also estimates and\nsuppresses any echoes from the loudspeaker(s) by using the echo reference\nsignals. We compare the performance of our proposed framework to several NB,\nSB, and full-band (FB) processing techniques in terms of speech quality and\nrecognition metrics. Based on experimental evaluations on simulated and\nreal-world recordings, we find that our proposed framework achieves better\nseparation performance over all SB and FB approaches and achieves performance\ncloser to NB processing techniques while requiring lower computing cost.\n", "date": "2022-11-22", "categories": []}
{"id": "2301.02886", "title": "Perceptual-Neural-Physical Sound Matching", "abstract": "  Sound matching algorithms seek to approximate a target waveform by parametric\naudio synthesis. Deep neural networks have achieved promising results in\nmatching sustained harmonic tones. However, the task is more challenging when\ntargets are nonstationary and inharmonic, e.g., percussion. We attribute this\nproblem to the inadequacy of loss function. On one hand, mean square error in\nthe parametric domain, known as \"P-loss\", is simple and fast but fails to\naccommodate the differing perceptual significance of each parameter. On the\nother hand, mean square error in the spectrotemporal domain, known as \"spectral\nloss\", is perceptually motivated and serves in differentiable digital signal\nprocessing (DDSP). Yet, spectral loss is a poor predictor of pitch intervals\nand its gradient may be computationally expensive; hence a slow convergence.\nAgainst this conundrum, we present Perceptual-Neural-Physical loss (PNP). PNP\nis the optimal quadratic approximation of spectral loss while being as fast as\nP-loss during training. We instantiate PNP with physical modeling synthesis as\ndecoder and joint time-frequency scattering transform (JTFS) as spectral\nrepresentation. We demonstrate its potential on matching synthetic drum sounds\nin comparison with other loss functions.\n", "date": "2023-01-07", "categories": []}
{"id": "2303.07143", "title": "Multi-Microphone Speaker Separation by Spatial Regions", "abstract": "  We consider the task of region-based source separation of reverberant\nmulti-microphone recordings. We assume pre-defined spatial regions with a\nsingle active source per region. The objective is to estimate the signals from\nthe individual spatial regions as captured by a reference microphone while\nretaining a correspondence between signals and spatial regions. We propose a\ndata-driven approach using a modified version of a state-of-the-art network,\nwhere different layers model spatial and spectro-temporal information. The\nnetwork is trained to enforce a fixed mapping of regions to network outputs.\nUsing speech from LibriMix, we construct a data set specifically designed to\ncontain the region information. Additionally, we train the network with\npermutation invariant training. We show that both training methods result in a\nfixed mapping of regions to network outputs, achieve comparable performance,\nand that the networks exploit spatial information. The proposed network\noutperforms a baseline network by 1.5 dB in scale-invariant\nsignal-to-distortion ratio.\n", "date": "2023-03-13", "categories": []}
{"id": "2208.08082", "title": "A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep\n  Learning", "abstract": "  The selective fixed-filter active noise control (SFANC) method selecting the\nbest pre-trained control filters for various types of noise can achieve a fast\nresponse time. However, it may lead to large steady-state errors due to\ninaccurate filter selection and the lack of adaptability. In comparison, the\nfiltered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower\nsteady-state errors through adaptive optimization. Nonetheless, its slow\nconvergence has a detrimental effect on dynamic noise attenuation. Therefore,\nthis paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive\nalgorithm's slow convergence and provide a better noise reduction level than\nthe SFANC method. A lightweight one-dimensional convolutional neural network\n(1D CNN) is designed to automatically select the most suitable pre-trained\ncontrol filter for each frame of the primary noise. Meanwhile, the FxNLMS\nalgorithm continues to update the coefficients of the chosen pre-trained\ncontrol filter at the sampling rate. Owing to the effective combination of the\ntwo algorithms, experimental results show that the hybrid SFANC-FxNLMS\nalgorithm can achieve a rapid response time, a low noise reduction error, and a\nhigh degree of robustness.\n", "date": "2022-08-17", "categories": []}
{"id": "2210.15305", "title": "Deformable Temporal Convolutional Networks for Monaural Noisy\n  Reverberant Speech Separation", "abstract": "  Speech separation models are used for isolating individual speakers in many\nspeech processing applications. Deep learning models have been shown to lead to\nstate-of-the-art (SOTA) results on a number of speech separation benchmarks.\nOne such class of models known as temporal convolutional networks (TCNs) has\nshown promising results for speech separation tasks. A limitation of these\nmodels is that they have a fixed receptive field (RF). Recent research in\nspeech dereverberation has shown that the optimal RF of a TCN varies with the\nreverberation characteristics of the speech signal. In this work deformable\nconvolution is proposed as a solution to allow TCN models to have dynamic RFs\nthat can adapt to various reverberation times for reverberant speech\nseparation. The proposed models are capable of achieving an 11.1 dB average\nscale-invariant signalto-distortion ratio (SISDR) improvement over the input\nsignal on the WHAMR benchmark. A relatively small deformable TCN model of 1.3M\nparameters is proposed which gives comparable separation performance to larger\nand more computationally complex models.\n", "date": "2022-10-27", "categories": []}
{"id": "2210.16984", "title": "Synthesizer Preset Interpolation using Transformer Auto-Encoders", "abstract": "  Sound synthesizers are widespread in modern music production but they\nincreasingly require expert skills to be mastered. This work focuses on\ninterpolation between presets, i.e., sets of values of all sound synthesis\nparameters, to enable the intuitive creation of new sounds from existing ones.\n  We introduce a bimodal auto-encoder neural network, which simultaneously\nprocesses presets using multi-head attention blocks, and audio using\nconvolutions. This model has been tested on a popular frequency modulation\nsynthesizer with more than one hundred parameters. Experiments have compared\nthe model to related architectures and methods, and have demonstrated that it\nperforms smoother interpolations. After training, the proposed model can be\nintegrated into commercial synthesizers for live interpolation or sound design\ntasks.\n", "date": "2022-10-27", "categories": []}
{"id": "2211.05385", "title": "GANStrument: Adversarial Instrument Sound Synthesis with Pitch-invariant\n  Instance Conditioning", "abstract": "  We propose GANStrument, a generative adversarial model for instrument sound\nsynthesis. Given a one-shot sound as input, it is able to generate pitched\ninstrument sounds that reflect the timbre of the input within an interactive\ntime. By exploiting instance conditioning, GANStrument achieves better fidelity\nand diversity of synthesized sounds and generalization ability to various\ninputs. In addition, we introduce an adversarial training scheme for a\npitch-invariant feature extractor that significantly improves the pitch\naccuracy and timbre consistency. Experimental results show that GANStrument\noutperforms strong baselines that do not use instance conditioning in terms of\ngeneration quality and input editability. Qualitative examples are available\nonline.\n", "date": "2022-11-10", "categories": []}
{"id": "2303.03666", "title": "Face: Fast, Accurate and Context-Aware Audio Annotation and\n  Classification", "abstract": "  This paper presents a context-aware framework for feature selection and\nclassification procedures to realize a fast and accurate audio event annotation\nand classification. The context-aware design starts with exploring feature\nextraction techniques to find an appropriate combination to select a set\nresulting in remarkable classification accuracy with minimal computational\neffort. The exploration for feature selection also embraces an investigation of\naudio Tempo representation, an advantageous feature extraction method missed by\nprevious works in the environmental audio classification research scope. The\nproposed annotation method considers outlier, inlier, and hard-to-predict data\nsamples to realize context-aware Active Learning, leading to the average\naccuracy of 90% when only 15% of data possess initial annotation. Our proposed\nalgorithm for sound classification obtained average prediction accuracy of\n98.05% on the UrbanSound8K dataset. The notebooks containing our source codes\nand implementation results are available at https://github.com/gitmehrdad/FACE.\n", "date": "2023-03-07", "categories": []}
{"id": "2303.03732", "title": "A Multi-Stage Triple-Path Method for Speech Separation in Noisy and\n  Reverberant Environments", "abstract": "  In noisy and reverberant environments, the performance of deep learning-based\nspeech separation methods drops dramatically because previous methods are not\ndesigned and optimized for such situations. To address this issue, we propose a\nmulti-stage end-to-end learning method that decouples the difficult speech\nseparation problem in noisy and reverberant environments into three\nsub-problems: speech denoising, separation, and de-reverberation. The\nprobability and speed of searching for the optimal solution of the speech\nseparation model are improved by reducing the solution space. Moreover, since\nthe channel information of the audio sequence in the time domain is crucial for\nspeech separation, we propose a triple-path structure capable of modeling the\nchannel dimension of audio sequences. Experimental results show that the\nproposed multi-stage triple-path method can improve the performance of speech\nseparation models at the cost of little model parameter increment.\n", "date": "2023-03-07", "categories": []}
{"id": "2303.03737", "title": "Multi-Dimensional and Multi-Scale Modeling for Speech Separation\n  Optimized by Discriminative Learning", "abstract": "  Transformer has shown advanced performance in speech separation, benefiting\nfrom its ability to capture global features. However, capturing local features\nand channel information of audio sequences in speech separation is equally\nimportant. In this paper, we present a novel approach named Intra-SE-Conformer\nand Inter-Transformer (ISCIT) for speech separation. Specifically, we design a\nnew network SE-Conformer that can model audio sequences in multiple dimensions\nand scales, and apply it to the dual-path speech separation framework.\nFurthermore, we propose Multi-Block Feature Aggregation to improve the\nseparation effect by selectively utilizing information from the intermediate\nblocks of the separation network. Meanwhile, we propose a speaker similarity\ndiscriminative loss to optimize the speech separation model to address the\nproblem of poor performance when speakers have similar voices. Experimental\nresults on the benchmark datasets WSJ0-2mix and WHAM! show that ISCIT can\nachieve state-of-the-art results.\n", "date": "2023-03-07", "categories": []}
{"id": "2207.04213", "title": "Dual-Path Cross-Modal Attention for better Audio-Visual Speech\n  Extraction", "abstract": "  Audio-visual target speech extraction, which aims to extract a certain\nspeaker's speech from the noisy mixture by looking at lip movements, has made\nsignificant progress combining time-domain speech separation models and visual\nfeature extractors (CNN). One problem of fusing audio and video information is\nthat they have different time resolutions. Most current research upsamples the\nvisual features along the time dimension so that audio and video features are\nable to align in time. However, we believe that lip movement should mostly\ncontain long-term, or phone-level information. Based on this assumption, we\npropose a new way to fuse audio-visual features. We observe that for DPRNN\n\\cite{dprnn}, the interchunk dimension's time resolution could be very close to\nthe time resolution of video frames. Like \\cite{sepformer}, the LSTM in DPRNN\nis replaced by intra-chunk and inter-chunk self-attention, but in the proposed\nalgorithm, inter-chunk attention incorporates the visual features as an\nadditional feature stream. This prevents the upsampling of visual cues,\nresulting in more efficient audio-visual fusion. The result shows we achieve\nsuperior results compared with other time-domain based audio-visual fusion\nmodels.\n", "date": "2022-07-09", "categories": []}
{"id": "2210.12108", "title": "Adversarial Permutation Invariant Training for Universal Sound\n  Separation", "abstract": "  Universal sound separation consists of separating mixes with arbitrary sounds\nof different types, and permutation invariant training (PIT) is used to train\nsource agnostic models that do so. In this work, we complement PIT with\nadversarial losses but find it challenging with the standard formulation used\nin speech source separation. We overcome this challenge with a novel\nI-replacement context-based adversarial loss, and by training with multiple\ndiscriminators. Our experiments show that by simply improving the loss (keeping\nthe same model and dataset) we obtain a non-negligible improvement of 1.4 dB\nSI-SNRi in the reverberant FUSS dataset. We also find adversarial PIT to be\neffective at reducing spectral holes, ubiquitous in mask-based separation\nmodels, which highlights the potential relevance of adversarial losses for\nsource separation.\n", "date": "2022-10-21", "categories": []}
{"id": "2302.09908", "title": "A Sidecar Separator Can Convert a Single-Talker Speech Recognition\n  System to a Multi-Talker One", "abstract": "  Although automatic speech recognition (ASR) can perform well in common\nnon-overlapping environments, sustaining performance in multi-talker\noverlapping speech recognition remains challenging. Recent research revealed\nthat ASR model's encoder captures different levels of information with\ndifferent layers -- the lower layers tend to have more acoustic information,\nand the upper layers more linguistic. This inspires us to develop a Sidecar\nseparator to empower a well-trained ASR model for multi-talker scenarios by\nseparating the mixed speech embedding between two suitable layers. We\nexperimented with a wav2vec 2.0-based ASR model with a Sidecar mounted. By\nfreezing the parameters of the original model and training only the Sidecar\n(8.7 M, 8.4% of all parameters), the proposed approach outperforms the previous\nstate-of-the-art by a large margin for the 2-speaker mixed LibriMix dataset,\nreaching a word error rate (WER) of 10.36%; and obtains comparable results\n(7.56%) for LibriSpeechMix dataset when limited training.\n", "date": "2023-02-20", "categories": []}
{"id": "2303.02396", "title": "A General Framework for Learning Procedural Audio Models of\n  Environmental Sounds", "abstract": "  This paper introduces the Procedural (audio) Variational autoEncoder (ProVE)\nframework as a general approach to learning Procedural Audio PA models of\nenvironmental sounds with an improvement to the realism of the synthesis while\nmaintaining provision of control over the generated sound through adjustable\nparameters. The framework comprises two stages: (i) Audio Class Representation,\nin which a latent representation space is defined by training an audio\nautoencoder, and (ii) Control Mapping, in which a joint function of\nstatic/temporal control variables derived from the audio and a random sample of\nuniform noise is learned to replace the audio encoder. We demonstrate the use\nof ProVE through the example of footstep sound effects on various surfaces. Our\nresults show that ProVE models outperform both classical PA models and an\nadversarial-based approach in terms of sound fidelity, as measured by Fr\\'echet\nAudio Distance (FAD), Maximum Mean Discrepancy (MMD), and subjective\nevaluations, making them feasible tools for sound design workflows.\n", "date": "2023-03-04", "categories": []}
{"id": "2303.02599", "title": "Hybrid Y-Net Architecture for Singing Voice Separation", "abstract": "  This research paper presents a novel deep learning-based neural network\narchitecture, named Y-Net, for achieving music source separation. The proposed\narchitecture performs end-to-end hybrid source separation by extracting\nfeatures from both spectrogram and waveform domains. Inspired by the U-Net\narchitecture, Y-Net predicts a spectrogram mask to separate vocal sources from\na mixture signal. Our results demonstrate the effectiveness of the proposed\narchitecture for music source separation with fewer parameters. Overall, our\nwork presents a promising approach for improving the accuracy and efficiency of\nmusic source separation.\n", "date": "2023-03-05", "categories": []}
{"id": "2303.03005", "title": "Scaling strategies for on-device low-complexity source separation with\n  Conv-Tasnet", "abstract": "  Recently, several very effective neural approaches for single-channel speech\nseparation have been presented in the literature. However, due to the size and\ncomplexity of these models, their use on low-resource devices, e.g. for hearing\naids, and earphones, is still a challenge and established solutions are not\navailable yet. Although approaches based on either pruning or compressing\nneural models have been proposed, the design of a model architecture suitable\nfor a certain application domain often requires heuristic procedures not easily\nportable to different low-resource platforms. Given the modular nature of the\nwell-known Conv-Tasnet speech separation architecture, in this paper we\nconsider three parameters that directly control the overall size of the model,\nnamely: the number of residual blocks, the number of repetitions of the\nseparation blocks and the number of channels in the depth-wise convolutions,\nand experimentally evaluate how they affect the speech separation performance.\nIn particular, experiments carried out on the Libri2Mix show that the number of\ndilated 1D-Conv blocks is the most critical parameter and that the usage of\nextra-dilation in the residual blocks allows reducing the performance drop.\n", "date": "2023-03-06", "categories": []}
{"id": "2210.15887", "title": "Nonparallel High-Quality Audio Super Resolution with Domain Adaptation\n  and Resampling CycleGANs", "abstract": "  Neural audio super-resolution models are typically trained on low- and\nhigh-resolution audio signal pairs. Although these methods achieve highly\naccurate super-resolution if the acoustic characteristics of the input data are\nsimilar to those of the training data, challenges remain: the models suffer\nfrom quality degradation for out-of-domain data, and paired data are required\nfor training. To address these problems, we propose Dual-CycleGAN, a\nhigh-quality audio super-resolution method that can utilize unpaired data based\non two connected cycle consistent generative adversarial networks (CycleGAN).\nOur method decomposes the super-resolution method into domain adaptation and\nresampling processes to handle acoustic mismatch in the unpaired low- and\nhigh-resolution signals. The two processes are then jointly optimized within\nthe CycleGAN framework. Experimental results verify that the proposed method\nsignificantly outperforms conventional methods when paired data are not\navailable. Code and audio samples are available from\nhttps://chomeyama.github.io/DualCycleGAN-Demo/.\n", "date": "2022-10-28", "categories": []}
{"id": "2211.12232", "title": "AERO: Audio Super Resolution in the Spectral Domain", "abstract": "  We present AERO, a audio super-resolution model that processes speech and\nmusic signals in the spectral domain. AERO is based on an encoder-decoder\narchitecture with U-Net like skip connections. We optimize the model using both\ntime and frequency domain loss functions. Specifically, we consider a set of\nreconstruction losses together with perceptual ones in the form of adversarial\nand feature discriminator loss functions. To better handle phase information\nthe proposed method operates over the complex-valued spectrogram using two\nseparate channels. Unlike prior work which mainly considers low and high\nfrequency concatenation for audio super-resolution, the proposed method\ndirectly predicts the full frequency range. We demonstrate high performance\nacross a wide range of sample rates considering both speech and music. AERO\noutperforms the evaluated baselines considering Log-Spectral Distance, ViSQOL,\nand the subjective MUSHRA test. Audio samples and code are available at\nhttps://pages.cs.huji.ac.il/adiyoss-lab/aero\n", "date": "2022-11-22", "categories": []}
{"id": "2302.13063", "title": "Time-Variance Aware Real-Time Speech Enhancement", "abstract": "  Time-variant factors often occur in real-world full-duplex communication\napplications. Some of them are caused by the complex environment such as\nnon-stationary environmental noises and varying acoustic path while some are\ncaused by the communication system such as the dynamic delay between the\nfar-end and near-end signals. Current end-to-end deep neural network (DNN)\nbased methods usually model the time-variant components implicitly and can\nhardly handle the unpredictable time-variance in real-time speech enhancement.\nTo explicitly capture the time-variant components, we propose a dynamic kernel\ngeneration (DKG) module that can be introduced as a learnable plug-in to a\nDNN-based end-to-end pipeline. Specifically, the DKG module generates a\nconvolutional kernel regarding to each input audio frame, so that the DNN model\nis able to dynamically adjust its weights according to the input signal during\ninference. Experimental results verify that DKG module improves the performance\nof the model under time-variant scenarios, in the joint acoustic echo\ncancellation (AEC) and deep noise suppression (DNS) tasks.\n", "date": "2023-02-25", "categories": []}
{"id": "2302.13542", "title": "Continuous descriptor-based control for deep audio synthesis", "abstract": "  Despite significant advances in deep models for music generation, the use of\nthese techniques remains restricted to expert users. Before being democratized\namong musicians, generative models must first provide expressive control over\nthe generation, as this conditions the integration of deep generative models in\ncreative workflows. In this paper, we tackle this issue by introducing a deep\ngenerative audio model providing expressive and continuous descriptor-based\ncontrol, while remaining lightweight enough to be embedded in a hardware\nsynthesizer. We enforce the controllability of real-time generation by\nexplicitly removing salient musical features in the latent space using an\nadversarial confusion criterion. User-specified features are then reintroduced\nas additional conditioning information, allowing for continuous control of the\ngeneration, akin to a synthesizer knob. We assess the performance of our method\non a wide variety of sounds including instrumental, percussive and speech\nrecordings while providing both timbre and attributes transfer, allowing new\nways of generating sounds.\n", "date": "2023-02-27", "categories": []}
{"id": "2302.11824", "title": "MossFormer: Pushing the Performance Limit of Monaural Speech Separation\n  using Gated Single-Head Transformer with Convolution-Augmented Joint\n  Self-Attentions", "abstract": "  Transformer based models have provided significant performance improvements\nin monaural speech separation. However, there is still a performance gap\ncompared to a recent proposed upper bound. The major limitation of the current\ndual-path Transformer models is the inefficient modelling of long-range\nelemental interactions and local feature patterns. In this work, we achieve the\nupper bound by proposing a gated single-head transformer architecture with\nconvolution-augmented joint self-attentions, named \\textit{MossFormer}\n(\\textit{Mo}naural \\textit{s}peech \\textit{s}eparation Trans\\textit{Former}).\nTo effectively solve the indirect elemental interactions across chunks in the\ndual-path architecture, MossFormer employs a joint local and global\nself-attention architecture that simultaneously performs a full-computation\nself-attention on local chunks and a linearised low-cost self-attention over\nthe full sequence. The joint attention enables MossFormer model full-sequence\nelemental interaction directly. In addition, we employ a powerful attentive\ngating mechanism with simplified single-head self-attentions. Besides the\nattentive long-range modelling, we also augment MossFormer with convolutions\nfor the position-wise local pattern modelling. As a consequence, MossFormer\nsignificantly outperforms the previous models and achieves the state-of-the-art\nresults on WSJ0-2/3mix and WHAM!/WHAMR! benchmarks. Our model achieves the\nSI-SDRi upper bound of 21.2 dB on WSJ0-3mix and only 0.3 dB below the upper\nbound of 23.1 dB on WSJ0-2mix.\n", "date": "2023-02-23", "categories": []}
{"id": "2302.11131", "title": "Unifying Speech Enhancement and Separation with Gradient Modulation for\n  End-to-End Noise-Robust Speech Separation", "abstract": "  Recent studies in neural network-based monaural speech separation (SS) have\nachieved a remarkable success thanks to increasing ability of long sequence\nmodeling. However, they would degrade significantly when put under realistic\nnoisy conditions, as the background noise could be mistaken for speaker's\nspeech and thus interfere with the separated sources. To alleviate this\nproblem, we propose a novel network to unify speech enhancement and separation\nwith gradient modulation to improve noise-robustness. Specifically, we first\nbuild a unified network by combining speech enhancement (SE) and separation\nmodules, with multi-task learning for optimization, where SE is supervised by\nparallel clean mixture to reduce noise for downstream speech separation.\nFurthermore, in order to avoid suppressing valid speaker information when\nreducing noise, we propose a gradient modulation (GM) strategy to harmonize the\nSE and SS tasks from optimization view. Experimental results show that our\napproach achieves the state-of-the-art on large-scale Libri2Mix- and\nLibri3Mix-noisy datasets, with SI-SNRi results of 16.0 dB and 15.8 dB\nrespectively. Our code is available at GitHub.\n", "date": "2023-02-22", "categories": []}
{"id": "2210.05917", "title": "Enemy Spotted: in-game gun sound dataset for gunshot classification and\n  localization", "abstract": "  Recently, deep learning-based methods have drawn huge attention due to their\nsimple yet high performance without domain knowledge in sound classification\nand localization tasks. However, a lack of gun sounds in existing datasets has\nbeen a major obstacle to implementing a support system to spot criminals from\ntheir gunshots by leveraging deep learning models. Since the occurrence of\ngunshot is rare and unpredictable, it is impractical to collect gun sounds in\nthe real world. As an alternative, gun sounds can be obtained from an FPS game\nthat is designed to mimic real-world warfare. The recent FPS game offers a\nrealistic environment where we can safely collect gunshot data while simulating\neven dangerous situations. By exploiting the advantage of the game environment,\nwe construct a gunshot dataset, namely BGG, for the firearm classification and\ngunshot localization tasks. The BGG dataset consists of 37 different types of\nfirearms, distances, and directions between the sound source and a receiver. We\ncarefully verify that the in-game gunshot data has sufficient information to\nidentify the location and type of gunshots by training several sound\nclassification and localization baselines on the BGG dataset. Afterward, we\ndemonstrate that the accuracy of real-world firearm classification and\nlocalization tasks can be enhanced by utilizing the BGG dataset.\n", "date": "2022-10-12", "categories": []}
{"id": "2206.04658", "title": "BigVGAN: A Universal Neural Vocoder with Large-Scale Training", "abstract": "  Despite recent progress in generative adversarial network (GAN)-based\nvocoders, where the model generates raw waveform conditioned on acoustic\nfeatures, it is challenging to synthesize high-fidelity audio for numerous\nspeakers across various recording environments. In this work, we present\nBigVGAN, a universal vocoder that generalizes well for various\nout-of-distribution scenarios without fine-tuning. We introduce periodic\nactivation function and anti-aliased representation into the GAN generator,\nwhich brings the desired inductive bias for audio synthesis and significantly\nimproves audio quality. In addition, we train our GAN vocoder at the largest\nscale up to 112M parameters, which is unprecedented in the literature. We\nidentify and address the failure modes in large-scale GAN training for audio,\nwhile maintaining high-fidelity output without over-regularization. Our\nBigVGAN, trained only on clean speech (LibriTTS), achieves the state-of-the-art\nperformance for various zero-shot (out-of-distribution) conditions, including\nunseen speakers, languages, recording environments, singing voices, music, and\ninstrumental audio. We release our code and model at:\nhttps://github.com/NVIDIA/BigVGAN\n", "date": "2022-06-09", "categories": []}
{"id": "2302.04331", "title": "Short-Term Memory Convolutions", "abstract": "  The real-time processing of time series signals is a critical issue for many\nreal-life applications. The idea of real-time processing is especially\nimportant in audio domain as the human perception of sound is sensitive to any\nkind of disturbance in perceived signals, especially the lag between auditory\nand visual modalities. The rise of deep learning (DL) models complicated the\nlandscape of signal processing. Although they often have superior quality\ncompared to standard DSP methods, this advantage is diminished by higher\nlatency. In this work we propose novel method for minimization of inference\ntime latency and memory consumption, called Short-Term Memory Convolution\n(STMC) and its transposed counterpart. The main advantage of STMC is the low\nlatency comparable to long short-term memory (LSTM) networks. Furthermore, the\ntraining of STMC-based models is faster and more stable as the method is based\nsolely on convolutional neural networks (CNNs). In this study we demonstrate an\napplication of this solution to a U-Net model for a speech separation task and\nGhostNet model in acoustic scene classification (ASC) task. In case of speech\nseparation we achieved a 5-fold reduction in inference time and a 2-fold\nreduction in latency without affecting the output quality. The inference time\nfor ASC task was up to 4 times faster while preserving the original accuracy.\n", "date": "2023-02-08", "categories": []}
{"id": "2302.00286", "title": "Jointist: Simultaneous Improvement of Multi-instrument Transcription and\n  Music Source Separation via Joint Training", "abstract": "  In this paper, we introduce Jointist, an instrument-aware multi-instrument\nframework that is capable of transcribing, recognizing, and separating multiple\nmusical instruments from an audio clip. Jointist consists of an instrument\nrecognition module that conditions the other two modules: a transcription\nmodule that outputs instrument-specific piano rolls, and a source separation\nmodule that utilizes instrument information and transcription results. The\njoint training of the transcription and source separation modules serves to\nimprove the performance of both tasks. The instrument module is optional and\ncan be directly controlled by human users. This makes Jointist a flexible\nuser-controllable framework. Our challenging problem formulation makes the\nmodel highly useful in the real world given that modern popular music typically\nconsists of multiple instruments. Its novelty, however, necessitates a new\nperspective on how to evaluate such a model. In our experiments, we assess the\nproposed model from various aspects, providing a new evaluation perspective for\nmulti-instrument transcription. Our subjective listening study shows that\nJointist achieves state-of-the-art performance on popular music, outperforming\nexisting multi-instrument transcription models such as MT3. We conducted\nexperiments on several downstream tasks and found that the proposed method\nimproved transcription by more than 1 percentage points (ppt.), source\nseparation by 5 SDR, downbeat detection by 1.8 ppt., chord recognition by 1.4\nppt., and key estimation by 1.4 ppt., when utilizing transcription results\nobtained from Jointist.\n  Demo available at \\url{https://jointist.github.io/Demo}.\n", "date": "2023-02-01", "categories": []}
{"id": "2301.13380", "title": "Automated Time-frequency Domain Audio Crossfades using Graph Cuts", "abstract": "  The problem of transitioning smoothly from one audio clip to another arises\nin many music consumption scenarios, especially as music consumption has moved\nfrom professionally curated and live-streamed radios to personal playback\ndevices and services. we present the first steps toward a new method of\nautomatically transitioning from one audio clip to another by discretizing the\nfrequency spectrum into bins and then finding transition times for each bin. We\nphrase the problem as one of graph flow optimization; specifically\nmin-cut/max-flow.\n", "date": "2023-01-31", "categories": []}
{"id": "2301.10183", "title": "Mesostructures: Beyond Spectrogram Loss in Differentiable Time-Frequency\n  Analysis", "abstract": "  Computer musicians refer to mesostructures as the intermediate levels of\narticulation between the microstructure of waveshapes and the macrostructure of\nmusical forms. Examples of mesostructures include melody, arpeggios,\nsyncopation, polyphonic grouping, and textural contrast. Despite their central\nrole in musical expression, they have received limited attention in deep\nlearning. Currently, autoencoders and neural audio synthesizers are only\ntrained and evaluated at the scale of microstructure: i.e., local amplitude\nvariations up to 100 milliseconds or so. In this paper, we formulate and\naddress the problem of mesostructural audio modeling via a composition of a\ndifferentiable arpeggiator and time-frequency scattering. We empirically\ndemonstrate that time--frequency scattering serves as a differentiable model of\nsimilarity between synthesis parameters that govern mesostructure. By exposing\nthe sensitivity of short-time spectral distances to time alignment, we motivate\nthe need for a time-invariant and multiscale differentiable time--frequency\nmodel of similarity at the level of both local spectra and spectrotemporal\nmodulations.\n", "date": "2023-01-24", "categories": []}
{"id": "2301.09027", "title": "Cellular Network Speech Enhancement: Removing Background and\n  Transmission Noise", "abstract": "  The primary objective of speech enhancement is to reduce background noise\nwhile preserving the target's speech. A common dilemma occurs when a speaker is\nconfined to a noisy environment and receives a call with high background and\ntransmission noise. To address this problem, the Deep Noise Suppression (DNS)\nChallenge focuses on removing the background noise with the next-generation\ndeep learning models to enhance the target's speech; however, researchers fail\nto consider Voice Over IP (VoIP) applications their transmission noise.\nFocusing on Google Meet and its cellular application, our work achieves\nstate-of-the-art performance on the Google Meet To Phone Track of the VoIP DNS\nChallenge. This paper demonstrates how to beat industrial performance and\nachieve 1.92 PESQ and 0.88 STOI, as well as superior acoustic fidelity,\nperceptual quality, and intelligibility in various metrics.\n", "date": "2023-01-22", "categories": []}
{"id": "2212.14618", "title": "Blind Restoration of Real-World Audio by 1D Operational GANs", "abstract": "  Objective: Despite numerous studies proposed for audio restoration in the\nliterature, most of them focus on an isolated restoration problem such as\ndenoising or dereverberation, ignoring other artifacts. Moreover, assuming a\nnoisy or reverberant environment with limited number of fixed\nsignal-to-distortion ratio (SDR) levels is a common practice. However,\nreal-world audio is often corrupted by a blend of artifacts such as\nreverberation, sensor noise, and background audio mixture with varying types,\nseverities, and duration. In this study, we propose a novel approach for blind\nrestoration of real-world audio signals by Operational Generative Adversarial\nNetworks (Op-GANs) with temporal and spectral objective metrics to enhance the\nquality of restored audio signal regardless of the type and severity of each\nartifact corrupting it. Methods: 1D Operational-GANs are used with generative\nneuron model optimized for blind restoration of any corrupted audio signal.\nResults: The proposed approach has been evaluated extensively over the\nbenchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with\na random blend of artifacts each with a random severity to mimic real-world\naudio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved,\nrespectively, which are substantial when compared with the baseline methods.\nSignificance: This is a pioneer study in blind audio restoration with the\nunique capability of direct (time-domain) restoration of real-world audio\nwhilst achieving an unprecedented level of performance for a wide SDR range and\nartifact types. Conclusion: 1D Op-GANs can achieve robust and computationally\neffective real-world audio restoration with significantly improved performance.\nThe source codes and the generated real-world audio datasets are shared\npublicly with the research community in a dedicated GitHub repository1.\n", "date": "2022-12-30", "categories": []}
{"id": "2301.08562", "title": "Latent Autoregressive Source Separation", "abstract": "  Autoregressive models have achieved impressive results over a wide range of\ndomains in terms of generation quality and downstream task performance. In the\ncontinuous domain, a key factor behind this success is the usage of quantized\nlatent spaces (e.g., obtained via VQ-VAE autoencoders), which allow for\ndimensionality reduction and faster inference times. However, using existing\npre-trained models to perform new non-trivial tasks is difficult since it\nrequires additional fine-tuning or extensive training to elicit prompting. This\npaper introduces LASS as a way to perform vector-quantized Latent\nAutoregressive Source Separation (i.e., de-mixing an input signal into its\nconstituent sources) without requiring additional gradient-based optimization\nor modifications of existing models. Our separation method relies on the\nBayesian formulation in which the autoregressive models are the priors, and a\ndiscrete (non-parametric) likelihood function is constructed by performing\nfrequency counts over latent sums of addend tokens. We test our method on\nimages and audio with several sampling strategies (e.g., ancestral, beam\nsearch) showing competitive results with existing approaches in terms of\nseparation quality while offering at the same time significant speedups in\nterms of inference time and scalability to higher dimensional data.\n", "date": "2023-01-09", "categories": []}
{"id": "2301.07665", "title": "An investigation of the reconstruction capacity of stacked convolutional\n  autoencoders for log-mel-spectrograms", "abstract": "  In audio processing applications, the generation of expressive sounds based\non high-level representations demonstrates a high demand. These representations\ncan be used to manipulate the timbre and influence the synthesis of creative\ninstrumental notes. Modern algorithms, such as neural networks, have inspired\nthe development of expressive synthesizers based on musical instrument timbre\ncompression. Unsupervised deep learning methods can achieve audio compression\nby training the network to learn a mapping from waveforms or spectrograms to\nlow-dimensional representations. This study investigates the use of stacked\nconvolutional autoencoders for the compression of time-frequency audio\nrepresentations for a variety of instruments for a single pitch. Further\nexploration of hyper-parameters and regularization techniques is demonstrated\nto enhance the performance of the initial design. In an unsupervised manner,\nthe network is able to reconstruct a monophonic and harmonic sound based on\nlatent representations. In addition, we introduce an evaluation metric to\nmeasure the similarity between the original and reconstructed samples.\nEvaluating a deep generative model for the synthesis of sound is a challenging\ntask. Our approach is based on the accuracy of the generated frequencies as it\npresents a significant metric for the perception of harmonic sounds. This work\nis expected to accelerate future experiments on audio compression using neural\nautoencoders.\n", "date": "2023-01-18", "categories": []}
{"id": "2301.06277", "title": "Improving Target Speaker Extraction with Sparse LDA-transformed Speaker\n  Embeddings", "abstract": "  As a practical alternative of speech separation, target speaker extraction\n(TSE) aims to extract the speech from the desired speaker using additional\nspeaker cue extracted from the speaker. Its main challenge lies in how to\nproperly extract and leverage the speaker cue to benefit the extracted speech\nquality. The cue extraction method adopted in majority existing TSE studies is\nto directly utilize discriminative speaker embedding, which is extracted from\nthe pre-trained models for speaker verification. Although the high speaker\ndiscriminability is a most desirable property for speaker verification task, we\nargue that it may be too sophisticated for TSE. In this study, we propose that\na simplified speaker cue with clear class separability might be preferred for\nTSE. To verify our proposal, we introduce several forms of speaker cues,\nincluding naive speaker embedding (such as, x-vector and xi-vector) and new\nspeaker embeddings produced from sparse LDA-transform. Corresponding TSE models\nare built by integrating these speaker cues with SepFormer (one SOTA speech\nseparation model). Performances of these TSE models are examined on the\nbenchmark WSJ0-2mix dataset. Experimental results validate the effectiveness\nand generalizability of our proposal, showing up to 9.9% relative improvement\nin SI-SDRi. Moreover, with SI-SDRi of 19.4 dB and PESQ of 3.78, our best TSE\nsystem significantly outperforms the current SOTA systems and offers the top\nTSE results reported till date on the WSJ0-2mix.\n", "date": "2023-01-16", "categories": []}
{"id": "2212.11277", "title": "Audio Denoising for Robust Audio Fingerprinting", "abstract": "  Music discovery services let users identify songs from short mobile\nrecordings. These solutions are often based on Audio Fingerprinting, and rely\nmore specifically on the extraction of spectral peaks in order to be robust to\na number of distortions. Few works have been done to study the robustness of\nthese algorithms to background noise captured in real environments. In\nparticular, AFP systems still struggle when the signal to noise ratio is low,\ni.e when the background noise is strong. In this project, we tackle this\nproblematic with Deep Learning. We test a new hybrid strategy which consists of\ninserting a denoising DL model in front of a peak-based AFP algorithm. We\nsimulate noisy music recordings using a realistic data augmentation pipeline,\nand train a DL model to denoise them. The denoising model limits the impact of\nbackground noise on the AFP system's extracted peaks, improving its robustness\nto noise. We further propose a novel loss function to adapt the DL model to the\nconsidered AFP system, increasing its precision in terms of retrieved spectral\npeaks. To the best of our knowledge, this hybrid strategy has not been tested\nbefore.\n", "date": "2022-12-21", "categories": []}
{"id": "2212.05360", "title": "Synthetic Wave-Geometric Impulse Responses for Improved Speech\n  Dereverberation", "abstract": "  We present a novel approach to improve the performance of learning-based\nspeech dereverberation using accurate synthetic datasets. Our approach is\ndesigned to recover the reverb-free signal from a reverberant speech signal. We\nshow that accurately simulating the low-frequency components of Room Impulse\nResponses (RIRs) is important to achieving good dereverberation. We use the GWA\ndataset that consists of synthetic RIRs generated in a hybrid fashion: an\naccurate wave-based solver is used to simulate the lower frequencies and\ngeometric ray tracing methods simulate the higher frequencies. We demonstrate\nthat speech dereverberation models trained on hybrid synthetic RIRs outperform\nmodels trained on RIRs generated by prior geometric ray tracing methods on four\nreal-world RIR datasets.\n", "date": "2022-12-10", "categories": []}
{"id": "2212.03814", "title": "iQuery: Instruments as Queries for Audio-Visual Sound Separation", "abstract": "  Current audio-visual separation methods share a standard architecture design\nwhere an audio encoder-decoder network is fused with visual encoding features\nat the encoder bottleneck. This design confounds the learning of multi-modal\nfeature encoding with robust sound decoding for audio separation. To generalize\nto a new instrument: one must finetune the entire visual and audio network for\nall musical instruments. We re-formulate visual-sound separation task and\npropose Instrument as Query (iQuery) with a flexible query expansion mechanism.\nOur approach ensures cross-modal consistency and cross-instrument\ndisentanglement. We utilize \"visually named\" queries to initiate the learning\nof audio queries and use cross-modal attention to remove potential sound source\ninterference at the estimated waveforms. To generalize to a new instrument or\nevent class, drawing inspiration from the text-prompt design, we insert an\nadditional query as an audio prompt while freezing the attention mechanism.\nExperimental results on three benchmarks demonstrate that our iQuery improves\naudio-visual sound source separation performance.\n", "date": "2022-12-07", "categories": []}
{"id": "2212.03401", "title": "MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware\n  Beamforming Network for Speech Separation", "abstract": "  Recently, many deep learning based beamformers have been proposed for\nmulti-channel speech separation. Nevertheless, most of them rely on extra cues\nknown in advance, such as speaker feature, face image or directional\ninformation. In this paper, we propose an end-to-end beamforming network for\ndirection guided speech separation given merely the mixture signal, namely\nMIMO-DBnet. Specifically, we design a multi-channel input and multiple outputs\narchitecture to predict the direction-of-arrival based embeddings and\nbeamforming weights for each source. The precisely estimated directional\nembedding provides quite effective spatial discrimination guidance for the\nneural beamformer to offset the effect of phase wrapping, thus allowing more\naccurate reconstruction of two sources' speech signals. Experiments show that\nour proposed MIMO-DBnet not only achieves a comprehensive decent improvement\ncompared to baseline systems, but also maintain the performance on high\nfrequency bands when phase wrapping occurs.\n", "date": "2022-12-07", "categories": []}
{"id": "2211.05172", "title": "Speech separation with large-scale self-supervised learning", "abstract": "  Self-supervised learning (SSL) methods such as WavLM have shown promising\nspeech separation (SS) results in small-scale simulation-based experiments. In\nthis work, we extend the exploration of the SSL-based SS by massively scaling\nup both the pre-training data (more than 300K hours) and fine-tuning data (10K\nhours). We also investigate various techniques to efficiently integrate the\npre-trained model with the SS network under a limited computation budget,\nincluding a low frame rate SSL model training setup and a fine-tuning scheme\nusing only the part of the pre-trained model. Compared with a supervised\nbaseline and the WavLM-based SS model using feature embeddings obtained with\nthe previously released 94K hours trained WavLM, our proposed model obtains\n15.9% and 11.2% of relative word error rate (WER) reductions, respectively, for\na simulated far-field speech mixture test set. For conversation transcription\non real meeting recordings using continuous speech separation, the proposed\nmodel achieves 6.8% and 10.6% of relative WER reductions over the purely\nsupervised baseline on AMI and ICSI evaluation sets, respectively, while\nreducing the computational cost by 38%.\n", "date": "2022-11-09", "categories": []}
{"id": "2211.15058", "title": "Mix and Localize: Localizing Sound Sources in Mixtures", "abstract": "  We present a method for simultaneously localizing multiple sound sources\nwithin a visual scene. This task requires a model to both group a sound mixture\ninto individual sources, and to associate them with a visual signal. Our method\njointly solves both tasks at once, using a formulation inspired by the\ncontrastive random walk of Jabri et al. We create a graph in which images and\nseparated sounds correspond to nodes, and train a random walker to transition\nbetween nodes from different modalities with high return probability. The\ntransition probabilities for this walk are determined by an audio-visual\nsimilarity metric that is learned by our model. We show through experiments\nwith musical instruments and human speech that our model can successfully\nlocalize multiple sounds, outperforming other self-supervised methods. Project\nsite: https://hxixixh.github.io/mix-and-localize\n", "date": "2022-11-28", "categories": []}
{"id": "2211.15524", "title": "Differentiable Dictionary Search: Integrating Linear Mixing with Deep\n  Non-Linear Modelling for Audio Source Separation", "abstract": "  This paper describes several improvements to a new method for signal\ndecomposition that we recently formulated under the name of Differentiable\nDictionary Search (DDS). The fundamental idea of DDS is to exploit a class of\npowerful deep invertible density estimators called normalizing flows, to model\nthe dictionary in a linear decomposition method such as NMF, effectively\ncreating a bijection between the space of dictionary elements and the\nassociated probability space, allowing a differentiable search through the\ndictionary space, guided by the estimated densities. As the initial formulation\nwas a proof of concept with some practical limitations, we will present several\nsteps towards making it scalable, hoping to improve both the computational\ncomplexity of the method and its signal decomposition capabilities. As a\ntestbed for experimental evaluation, we choose the task of frame-level piano\ntranscription, where the signal is to be decomposed into sources whose activity\nis attributed to individual piano notes. To highlight the impact of improved\nnon-linear modelling of sources, we compare variants of our method to a linear\novercomplete NMF baseline. Experimental results will show that even in the\nabsence of additional constraints, our models produce increasingly sparse and\nprecise decompositions, according to two pertinent evaluation measures.\n", "date": "2022-11-28", "categories": []}
{"id": "2209.12894", "title": "Biologically-Plausible Determinant Maximization Neural Networks for\n  Blind Separation of Correlated Sources", "abstract": "  Extraction of latent sources of complex stimuli is critical for making sense\nof the world. While the brain solves this blind source separation (BSS) problem\ncontinuously, its algorithms remain unknown. Previous work on\nbiologically-plausible BSS algorithms assumed that observed signals are linear\nmixtures of statistically independent or uncorrelated sources, limiting the\ndomain of applicability of these algorithms. To overcome this limitation, we\npropose novel biologically-plausible neural networks for the blind separation\nof potentially dependent/correlated sources. Differing from previous work, we\nassume some general geometric, not statistical, conditions on the source\nvectors allowing separation of potentially dependent/correlated sources.\nConcretely, we assume that the source vectors are sufficiently scattered in\ntheir domains which can be described by certain polytopes. Then, we consider\nrecovery of these sources by the Det-Max criterion, which maximizes the\ndeterminant of the output correlation matrix to enforce a similar spread for\nthe source estimates. Starting from this normative principle, and using a\nweighted similarity matching approach that enables arbitrary linear\ntransformations adaptable by local learning rules, we derive two-layer\nbiologically-plausible neural network algorithms that can separate mixtures\ninto sources coming from a variety of source domains. We demonstrate that our\nalgorithms outperform other biologically-plausible BSS algorithms on correlated\nsource separation problems.\n", "date": "2022-09-27", "categories": []}
{"id": "2207.07307", "title": "MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with\n  Unknown Number of Sound Sources", "abstract": "  Recent neural network based Direction of Arrival (DoA) estimation algorithms\nhave performed well on unknown number of sound sources scenarios. These\nalgorithms are usually achieved by mapping the multi-channel audio input to the\nsingle output (i.e. overall spatial pseudo-spectrum (SPS) of all sources), that\nis called MISO. However, such MISO algorithms strongly depend on empirical\nthreshold setting and the angle assumption that the angles between the sound\nsources are greater than a fixed angle. To address these limitations, we\npropose a novel multi-channel input and multiple outputs DoA network called\nMIMO-DoAnet. Unlike the general MISO algorithms, MIMO-DoAnet predicts the SPS\ncoding of each sound source with the help of the informative spatial covariance\nmatrix. By doing so, the threshold task of detecting the number of sound\nsources becomes an easier task of detecting whether there is a sound source in\neach output, and the serious interaction between sound sources disappears\nduring inference stage. Experimental results show that MIMO-DoAnet achieves\nrelative 18.6% and absolute 13.3%, relative 34.4% and absolute 20.2% F1 score\nimprovement compared with the MISO baseline system in 3, 4 sources scenes. The\nresults also demonstrate MIMO-DoAnet alleviates the threshold setting problem\nand solves the angle assumption problem effectively.\n", "date": "2022-07-15", "categories": []}
{"id": "2211.08715", "title": "Conditional variational autoencoder to improve neural audio synthesis\n  for polyphonic music sound", "abstract": "  Deep generative models for audio synthesis have recently been significantly\nimproved. However, the task of modeling raw-waveforms remains a difficult\nproblem, especially for audio waveforms and music signals. Recently, the\nrealtime audio variational autoencoder (RAVE) method was developed for\nhigh-quality audio waveform synthesis. The RAVE method is based on the\nvariational autoencoder and utilizes the two-stage training strategy.\nUnfortunately, the RAVE model is limited in reproducing wide-pitch polyphonic\nmusic sound. Therefore, to enhance the reconstruction performance, we adopt the\npitch activation data as an auxiliary information to the RAVE model. To handle\nthe auxiliary information, we propose an enhanced RAVE model with a conditional\nvariational autoencoder structure and an additional fully-connected layer. To\nevaluate the proposed structure, we conducted a listening experiment based on\nmultiple stimulus tests with hidden references and an anchor (MUSHRA) with the\nMAESTRO. The obtained results indicate that the proposed model exhibits a more\nsignificant performance and stability improvement than the conventional RAVE\nmodel.\n", "date": "2022-11-16", "categories": []}
{"id": "2211.08303", "title": "Reverberation as Supervision for Speech Separation", "abstract": "  This paper proposes reverberation as supervision (RAS), a novel unsupervised\nloss function for single-channel reverberant speech separation. Prior methods\nfor unsupervised separation required the synthesis of mixtures of mixtures or\nassumed the existence of a teacher model, making them difficult to consider as\npotential methods explaining the emergence of separation abilities in an\nanimal's auditory system. We assume the availability of two-channel mixtures at\ntraining time, and train a neural network to separate the sources given one of\nthe channels as input such that the other channel may be predicted from the\nseparated sources. As the relationship between the room impulse responses\n(RIRs) of each channel depends on the locations of the sources, which are\nunknown to the network, the network cannot rely on learning that relationship.\nInstead, our proposed loss function fits each of the separated sources to the\nmixture in the target channel via Wiener filtering, and compares the resulting\nmixture to the ground-truth one. We show that minimizing the scale-invariant\nsignal-to-distortion ratio (SI-SDR) of the predicted right-channel mixture with\nrespect to the ground truth implicitly guides the network towards separating\nthe left-channel sources. On a semi-supervised reverberant speech separation\ntask based on the WHAMR! dataset, using training data where just 5% (resp.,\n10%) of the mixtures are labeled with associated isolated sources, we achieve\n70% (resp., 78%) of the SI-SDR improvement obtained when training with\nsupervision on the full training set, while a model trained only on the labeled\ndata obtains 43% (resp., 45%).\n", "date": "2022-11-15", "categories": []}
{"id": "2211.05927", "title": "Optimal Condition Training for Target Source Separation", "abstract": "  Recent research has shown remarkable performance in leveraging multiple\nextraneous conditional and non-mutually exclusive semantic concepts for sound\nsource separation, allowing the flexibility to extract a given target source\nbased on multiple different queries. In this work, we propose a new optimal\ncondition training (OCT) method for single-channel target source separation,\nbased on greedy parameter updates using the highest performing condition among\nequivalent conditions associated with a given target source. Our experiments\nshow that the complementary information carried by the diverse semantic\nconcepts significantly helps to disentangle and isolate sources of interest\nmuch more efficiently compared to single-conditioned models. Moreover, we\npropose a variation of OCT with condition refinement, in which an initial\nconditional vector is adapted to the given mixture and transformed to a more\namenable representation for target source extraction. We showcase the\neffectiveness of OCT on diverse source separation experiments where it improves\nupon permutation invariant models with oracle assignment and obtains\nstate-of-the-art performance in the more challenging task of text-based source\nseparation, outperforming even dedicated text-only conditioned models.\n", "date": "2022-11-11", "categories": []}
{"id": "2210.16791", "title": "Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo\n  Cancellation with Supervised Contrastive Learning", "abstract": "  Acoustic echo cancellation (AEC) is designed to remove echoes, reverberation,\nand unwanted added sounds from the microphone signal while maintaining the\nquality of the near-end speaker's speech. This paper proposes adaptive speech\nquality complex neural networks to focus on specific tasks for real-time\nacoustic echo cancellation. In specific, we propose a complex modularize neural\nnetwork with different stages to focus on feature extraction, acoustic\nseparation, and mask optimization receptively. Furthermore, we adopt the\ncontrastive learning framework and novel speech quality aware loss functions to\nfurther improve the performance. The model is trained with 72 hours for\npre-training and then 72 hours for fine-tuning. The proposed model outperforms\nthe state-of-the-art performance.\n", "date": "2022-10-30", "categories": []}
{"id": "2211.04124", "title": "Unsupervised vocal dereverberation with diffusion-based generative\n  models", "abstract": "  Removing reverb from reverberant music is a necessary technique to clean up\naudio for downstream music manipulations. Reverberation of music contains two\ncategories, natural reverb, and artificial reverb. Artificial reverb has a\nwider diversity than natural reverb due to its various parameter setups and\nreverberation types. However, recent supervised dereverberation methods may\nfail because they rely on sufficiently diverse and numerous pairs of\nreverberant observations and retrieved data for training in order to be\ngeneralizable to unseen observations during inference. To resolve these\nproblems, we propose an unsupervised method that can remove a general kind of\nartificial reverb for music without requiring pairs of data for training. The\nproposed method is based on diffusion models, where it initializes the unknown\nreverberation operator with a conventional signal processing technique and\nsimultaneously refines the estimate with the help of diffusion models. We show\nthrough objective and perceptual evaluations that our method outperforms the\ncurrent leading vocal dereverberation benchmarks.\n", "date": "2022-11-08", "categories": []}
{"id": "2211.03202", "title": "\"Seeing Sound\": Audio Classification with the Wigner-Wille Distribution\n  and Convolutional Neural Networks", "abstract": "  With big data becoming increasingly available, IoT hardware becoming widely\nadopted, and AI capabilities becoming more powerful, organizations are\ncontinuously investing in sensing. Data coming from sensor networks are\ncurrently combined with sensor fusion and AI algorithms to drive innovation in\nfields such as self-driving cars. Data from these sensors can be utilized in\nnumerous use cases, including alerts in safety systems of urban settings, for\nevents such as gun shots and explosions. Moreover, diverse types of sensors,\nsuch as sound sensors, can be utilized in low-light conditions or at locations\nwhere a camera is not available. This paper investigates the potential of the\nutilization of sound-sensor data in an urban context. Technically, we propose a\nnovel approach of classifying sound data using the Wigner-Ville distribution\nand Convolutional Neural Networks. In this paper, we report on the performance\nof the approach on open-source datasets. The concept and work presented is\nbased on my doctoral thesis, which was performed as part of the Engineering\nDoctorate program in Data Science at the University of Eindhoven, in\ncollaboration with the Dutch National Police. Additional work on real-world\ndatasets was performed during the thesis, which are not presented here due to\nconfidentiality.\n", "date": "2022-11-06", "categories": []}
{"id": "2211.02301", "title": "Binaural Rendering of Ambisonic Signals by Neural Networks", "abstract": "  Binaural rendering of ambisonic signals is of broad interest to virtual\nreality and immersive media. Conventional methods often require manually\nmeasured Head-Related Transfer Functions (HRTFs). To address this issue, we\ncollect a paired ambisonic-binaural dataset and propose a deep learning\nframework in an end-to-end manner. Experimental results show that neural\nnetworks outperform the conventional method in objective metrics and achieve\ncomparable subjective metrics. To validate the proposed framework, we\nexperimentally explore different settings of the input features, model\nstructures, output features, and loss functions. Our proposed system achieves\nan SDR of 7.32 and MOSs of 3.83, 3.58, 3.87, 3.58 in quality, timbre,\nlocalization, and immersion dimensions.\n", "date": "2022-11-04", "categories": []}
{"id": "2210.17327", "title": "Diffusion-based Generative Speech Source Separation", "abstract": "  We propose DiffSep, a new single channel source separation method based on\nscore-matching of a stochastic differential equation (SDE). We craft a tailored\ncontinuous time diffusion-mixing process starting from the separated sources\nand converging to a Gaussian distribution centered on their mixture. This\nformulation lets us apply the machinery of score-based generative modelling.\nFirst, we train a neural network to approximate the score function of the\nmarginal probabilities or the diffusion-mixing process. Then, we use it to\nsolve the reverse time SDE that progressively separates the sources starting\nfrom their mixture. We propose a modified training strategy to handle model\nmismatch and source permutation ambiguity. Experiments on the WSJ0 2mix dataset\ndemonstrate the potential of the method. Furthermore, the method is also\nsuitable for speech enhancement and shows performance competitive with prior\nwork on the VoiceBank-DEMAND dataset.\n", "date": "2022-10-31", "categories": []}
{"id": "2211.00988", "title": "Audio-visual speech enhancement with a deep Kalman filter generative\n  model", "abstract": "  Deep latent variable generative models based on variational autoencoder (VAE)\nhave shown promising performance for audiovisual speech enhancement (AVSE). The\nunderlying idea is to learn a VAEbased audiovisual prior distribution for clean\nspeech data, and then combine it with a statistical noise model to recover a\nspeech signal from a noisy audio recording and video (lip images) of the target\nspeaker. Existing generative models developed for AVSE do not take into account\nthe sequential nature of speech data, which prevents them from fully\nincorporating the power of visual data. In this paper, we present an\naudiovisual deep Kalman filter (AV-DKF) generative model which assumes a\nfirst-order Markov chain model for the latent variables and effectively fuses\naudiovisual data. Moreover, we develop an efficient inference methodology to\nestimate speech signals at test time. We conduct a set of experiments to\ncompare different variants of generative models for speech enhancement. The\nresults demonstrate the superiority of the AV-DKF model compared with both its\naudio-only version and the non-sequential audio-only and audiovisual VAE-based\nmodels.\n", "date": "2022-11-02", "categories": []}
{"id": "2211.01189", "title": "Inference and Denoise: Causal Inference-based Neural Speech Enhancement", "abstract": "  This study addresses the speech enhancement (SE) task within the causal\ninference paradigm by modeling the noise presence as an intervention. Based on\nthe potential outcome framework, the proposed causal inference-based speech\nenhancement (CISE) separates clean and noisy frames in an intervened noisy\nspeech using a noise detector and assigns both sets of frames to two mask-based\nenhancement modules (EMs) to perform noise-conditional SE. Specifically, we use\nthe presence of noise as guidance for EM selection during training, and the\nnoise detector selects the enhancement module according to the prediction of\nthe presence of noise for each frame. Moreover, we derived a SE-specific\naverage treatment effect to quantify the causal effect adequately. Experimental\nevidence demonstrates that CISE outperforms a non-causal mask-based SE approach\nin the studied settings and has better performance and efficiency than more\ncomplex SE models.\n", "date": "2022-11-02", "categories": []}
{"id": "2210.15750", "title": "One-Shot Acoustic Matching Of Audio Signals -- Learning to Hear Music In\n  Any Room/ Concert Hall", "abstract": "  The acoustic space in which a sound is created and heard plays an essential\nrole in how that sound is perceived by affording a unique sense of\n\\textit{presence}. Every sound we hear results from successive convolution\noperations intrinsic to the sound source and external factors such as\nmicrophone characteristics and room impulse responses. Typically, researchers\nuse an excitation such as a pistol shot or balloon pop as an impulse signal\nwith which an auralization can be created. The room \"impulse\" responses\nconvolved with the signal of interest can transform the input sound into the\nsound played in the acoustic space of interest. Here we propose a novel\narchitecture that can transform a sound of interest into any other acoustic\nspace(room or hall) of interest by using arbitrary audio recorded as a proxy\nfor a balloon pop. The architecture is grounded in simple signal processing\nideas to learn residual signals from a learned acoustic signature and the input\nsignal. Our framework allows a neural network to adjust gains of every point in\nthe time-frequency representation, giving sound qualitative and quantitative\nresults.\n", "date": "2022-10-27", "categories": []}
{"id": "2210.16472", "title": "Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source\n  Separation", "abstract": "  There exists an unequivocal distinction between the sound produced by a\nstatic source and that produced by a moving one, especially when the source\nmoves towards or away from the microphone. In this paper, we propose to use\nthis connection between audio and visual dynamics for solving two challenging\ntasks simultaneously, namely: (i) separating audio sources from a mixture using\nvisual cues, and (ii) predicting the 3D visual motion of a sounding source\nusing its separated audio. Towards this end, we present Audio Separator and\nMotion Predictor (ASMP) -- a deep learning framework that leverages the 3D\nstructure of the scene and the motion of sound sources for better audio source\nseparation. At the heart of ASMP is a 2.5D scene graph capturing various\nobjects in the video and their pseudo-3D spatial proximities. This graph is\nconstructed by registering together 2.5D monocular depth predictions from the\n2D video frames and associating the 2.5D scene regions with the outputs of an\nobject detector applied on those frames. The ASMP task is then mathematically\nmodeled as the joint problem of: (i) recursively segmenting the 2.5D scene\ngraph into several sub-graphs, each associated with a constituent sound in the\ninput audio mixture (which is then separated) and (ii) predicting the 3D\nmotions of the corresponding sound sources from the separated audio. To\nempirically evaluate ASMP, we present experiments on two challenging\naudio-visual datasets, viz. Audio Separation in the Wild (ASIW) and Audio\nVisual Event (AVE). Our results demonstrate that ASMP achieves a clear\nimprovement in source separation quality, outperforming prior works on both\ndatasets, while also estimating the direction of motion of the sound sources\nbetter than other methods.\n", "date": "2022-10-29", "categories": []}
{"id": "2210.15306", "title": "Rigid-Body Sound Synthesis with Differentiable Modal Resonators", "abstract": "  Physical models of rigid bodies are used for sound synthesis in applications\nfrom virtual environments to music production. Traditional methods such as\nmodal synthesis often rely on computationally expensive numerical solvers,\nwhile recent deep learning approaches are limited by post-processing of their\nresults. In this work we present a novel end-to-end framework for training a\ndeep neural network to generate modal resonators for a given 2D shape and\nmaterial, using a bank of differentiable IIR filters. We demonstrate our method\non a dataset of synthetic objects, but train our model using an audio-domain\nobjective, paving the way for physically-informed synthesisers to be learned\ndirectly from recordings of real-world objects.\n", "date": "2022-10-27", "categories": []}
{"id": "2210.15370", "title": "CasNet: Investigating Channel Robustness for Speech Separation", "abstract": "  Recording channel mismatch between training and testing conditions has been\nshown to be a serious problem for speech separation. This situation greatly\nreduces the separation performance, and cannot meet the requirement of daily\nuse. In this study, inheriting the use of our previously constructed TAT-2mix\ncorpus, we address the channel mismatch problem by proposing a channel-aware\naudio separation network (CasNet), a deep learning framework for end-to-end\ntime-domain speech separation. CasNet is implemented on top of TasNet. Channel\nembedding (characterizing channel information in a mixture of multiple\nutterances) generated by Channel Encoder is introduced into the separation\nmodule by the FiLM technique. Through two training strategies, we explore two\nroles that channel embedding may play: 1) a real-life noise disturbance, making\nthe model more robust, or 2) a guide, instructing the separation model to\nretain the desired channel information. Experimental results on TAT-2mix show\nthat CasNet trained with both training strategies outperforms the TasNet\nbaseline, which does not use channel embeddings.\n", "date": "2022-10-27", "categories": []}
{"id": "2210.14661", "title": "Full-band General Audio Synthesis with Score-based Diffusion", "abstract": "  Recent works have shown the capability of deep generative models to tackle\ngeneral audio synthesis from a single label, producing a variety of impulsive,\ntonal, and environmental sounds. Such models operate on band-limited signals\nand, as a result of an autoregressive approach, they are typically conformed by\npre-trained latent encoders and/or several cascaded modules. In this work, we\npropose a diffusion-based generative model for general audio synthesis, named\nDAG, which deals with full-band signals end-to-end in the waveform domain.\nResults show the superiority of DAG over existing label-conditioned generators\nin terms of both quality and diversity. More specifically, when compared to the\nstate of the art, the band-limited and full-band versions of DAG achieve\nrelative improvements that go up to 40 and 65%, respectively. We believe DAG is\nflexible enough to accommodate different conditioning schemas while providing\ngood quality synthesis.\n", "date": "2022-10-26", "categories": []}
{"id": "2207.10441", "title": "Deep Audio Waveform Prior", "abstract": "  Convolutional neural networks contain strong priors for generating natural\nlooking images [1]. These priors enable image denoising, super resolution, and\ninpainting in an unsupervised manner. Previous attempts to demonstrate similar\nideas in audio, namely deep audio priors, (i) use hand picked architectures\nsuch as harmonic convolutions, (ii) only work with spectrogram input, and (iii)\nhave been used mostly for eliminating Gaussian noise [2]. In this work we show\nthat existing SOTA architectures for audio source separation contain deep\npriors even when working with the raw waveform. Deep priors can be discovered\nby training a neural network to generate a single corrupted signal when given\nwhite noise as input. A network with relevant deep priors is likely to generate\na cleaner version of the signal before converging on the corrupted signal. We\ndemonstrate this restoration effect with several corruptions: background noise,\nreverberations, and a gap in the signal (audio inpainting).\n", "date": "2022-07-21", "categories": []}
{"id": "2210.13932", "title": "CoLoC: Conditioned Localizer and Classifier for Sound Event Localization\n  and Detection", "abstract": "  In this article, we describe Conditioned Localizer and Classifier (CoLoC)\nwhich is a novel solution for Sound Event Localization and Detection (SELD).\nThe solution constitutes of two stages: the localization is done first and is\nfollowed by classification conditioned by the output of the localizer. In order\nto resolve the problem of the unknown number of sources we incorporate the idea\nborrowed from Sequential Set Generation (SSG). Models from both stages are\nSELDnet-like CRNNs, but with single outputs. Conducted reasoning shows that\nsuch two single-output models are fit for SELD task. We show that our solution\nimproves on the baseline system in most metrics on the STARSS22 Dataset.\n", "date": "2022-10-25", "categories": []}
{"id": "2210.13438", "title": "High Fidelity Neural Audio Compression", "abstract": "  We introduce a state-of-the-art real-time, high-fidelity, audio codec\nleveraging neural networks. It consists in a streaming encoder-decoder\narchitecture with quantized latent space trained in an end-to-end fashion. We\nsimplify and speed-up the training by using a single multiscale spectrogram\nadversary that efficiently reduces artifacts and produce high-quality samples.\nWe introduce a novel loss balancer mechanism to stabilize training: the weight\nof a loss now defines the fraction of the overall gradient it should represent,\nthus decoupling the choice of this hyper-parameter from the typical scale of\nthe loss. Finally, we study how lightweight Transformer models can be used to\nfurther compress the obtained representation by up to 40%, while staying faster\nthan real time. We provide a detailed description of the key design choices of\nthe proposed model including: training objective, architectural changes and a\nstudy of various perceptual loss functions. We present an extensive subjective\nevaluation (MUSHRA tests) together with an ablation study for a range of\nbandwidths and audio domains, including speech, noisy-reverberant speech, and\nmusic. Our approach is superior to the baselines methods across all evaluated\nsettings, considering both 24 kHz monophonic and 48 kHz stereophonic audio.\nCode and models are available at github.com/facebookresearch/encodec.\n", "date": "2022-10-24", "categories": []}
{"id": "2210.10196", "title": "BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds", "abstract": "  Audio denoising has been explored for decades using both traditional and deep\nlearning-based methods. However, these methods are still limited to either\nmanually added artificial noise or lower denoised audio quality. To overcome\nthese challenges, we collect a large-scale natural noise bird sound dataset. We\nare the first to transfer the audio denoising problem into an image\nsegmentation problem and propose a deep visual audio denoising (DVAD) model.\nWith a total of 14,120 audio images, we develop an audio ImageMask tool and\npropose to use a few-shot generalization strategy to label these images.\nExtensive experimental results demonstrate that the proposed model achieves\nstate-of-the-art performance. We also show that our method can be easily\ngeneralized to speech denoising, audio separation, audio enhancement, and noise\nestimation.\n", "date": "2022-10-18", "categories": []}
{"id": "2210.07508", "title": "Hierarchical Diffusion Models for Singing Voice Neural Vocoder", "abstract": "  Recent progress in deep generative models has improved the quality of neural\nvocoders in speech domain. However, generating a high-quality singing voice\nremains challenging due to a wider variety of musical expressions in pitch,\nloudness, and pronunciations. In this work, we propose a hierarchical diffusion\nmodel for singing voice neural vocoders. The proposed method consists of\nmultiple diffusion models operating in different sampling rates; the model at\nthe lowest sampling rate focuses on generating accurate low-frequency\ncomponents such as pitch, and other models progressively generate the waveform\nat higher sampling rates on the basis of the data at the lower sampling rate\nand acoustic features. Experimental results show that the proposed method\nproduces high-quality singing voices for multiple singers, outperforming\nstate-of-the-art neural vocoders with a similar range of computational costs.\n", "date": "2022-10-14", "categories": []}
{"id": "2210.08610", "title": "Robust, General, and Low Complexity Acoustic Scene Classification\n  Systems and An Effective Visualization for Presenting a Sound Scene Context", "abstract": "  In this paper, we present a comprehensive analysis of Acoustic Scene\nClassification (ASC), the task of identifying the scene of an audio recording\nfrom its acoustic signature. In particular, we firstly propose an\ninception-based and low footprint ASC model, referred to as the ASC baseline.\nThe proposed ASC baseline is then compared with benchmark and high-complexity\nnetwork architectures of MobileNetV1, MobileNetV2, VGG16, VGG19, ResNet50V2,\nResNet152V2, DenseNet121, DenseNet201, and Xception. Next, we improve the ASC\nbaseline by proposing a novel deep neural network architecture which leverages\nresidual-inception architectures and multiple kernels. Given the novel\nresidual-inception (NRI) model, we further evaluate the trade off between the\nmodel complexity and the model accuracy performance. Finally, we evaluate\nwhether sound events occurring in a sound scene recording can help to improve\nASC accuracy, then indicate how a sound scene context is well presented by\ncombining both sound scene and sound event information. We conduct extensive\nexperiments on various ASC datasets, including Crowded Scenes, IEEE AASP\nChallenge on Detection and Classification of Acoustic Scenes and Events (DCASE)\n2018 Task 1A and 1B, 2019 Task 1A and 1B, 2020 Task 1A, 2021 Task 1A, 2022 Task\n1. The experimental results on several different ASC challenges highlight two\nmain achievements; the first is to propose robust, general, and low complexity\nASC systems which are suitable for real-life applications on a wide range of\nedge devices and mobiles; the second is to propose an effective visualization\nmethod for comprehensively presenting a sound scene context.\n", "date": "2022-10-16", "categories": []}
{"id": "2210.06177", "title": "VCSE: Time-Domain Visual-Contextual Speaker Extraction Network", "abstract": "  Speaker extraction seeks to extract the target speech in a multi-talker\nscenario given an auxiliary reference. Such reference can be auditory, i.e., a\npre-recorded speech, visual, i.e., lip movements, or contextual, i.e., phonetic\nsequence. References in different modalities provide distinct and complementary\ninformation that could be fused to form top-down attention on the target\nspeaker. Previous studies have introduced visual and contextual modalities in a\nsingle model. In this paper, we propose a two-stage time-domain\nvisual-contextual speaker extraction network named VCSE, which incorporates\nvisual and self-enrolled contextual cues stage by stage to take full advantage\nof every modality. In the first stage, we pre-extract a target speech with\nvisual cues and estimate the underlying phonetic sequence. In the second stage,\nwe refine the pre-extracted target speech with the self-enrolled contextual\ncues. Experimental results on the real-world Lip Reading Sentences 3 (LRS3)\ndatabase demonstrate that our proposed VCSE network consistently outperforms\nother state-of-the-art baselines.\n", "date": "2022-10-09", "categories": []}
{"id": "2210.06368", "title": "Individualized Conditioning and Negative Distances for Speaker\n  Separation", "abstract": "  Speaker separation aims to extract multiple voices from a mixed signal. In\nthis paper, we propose two speaker-aware designs to improve the existing\nspeaker separation solutions. The first model is a speaker conditioning network\nthat integrates speech samples to generate individualized speaker conditions,\nwhich then provide informed guidance for a separation module to produce\nwell-separated outputs.\n  The second design aims to reduce non-target voices in the separated speech.\nTo this end, we propose negative distances to penalize the appearance of any\nnon-target voice in the channel outputs, and positive distances to drive the\nseparated voices closer to the clean targets. We explore two different setups,\nweighted-sum and triplet-like, to integrate these two distances to form a\ncombined auxiliary loss for the separation networks. Experiments conducted on\nLibriMix demonstrate the effectiveness of our proposed models.\n", "date": "2022-10-12", "categories": []}
{"id": "2209.15174", "title": "Music Source Separation with Band-split RNN", "abstract": "  The performance of music source separation (MSS) models has been greatly\nimproved in recent years thanks to the development of novel neural network\narchitectures and training pipelines. However, recent model designs for MSS\nwere mainly motivated by other audio processing tasks or other research fields,\nwhile the intrinsic characteristics and patterns of the music signals were not\nfully discovered. In this paper, we propose band-split RNN (BSRNN), a\nfrequency-domain model that explictly splits the spectrogram of the mixture\ninto subbands and perform interleaved band-level and sequence-level modeling.\nThe choices of the bandwidths of the subbands can be determined by a priori\nknowledge or expert knowledge on the characteristics of the target source in\norder to optimize the performance on a certain type of target musical\ninstrument. To better make use of unlabeled data, we also describe a\nsemi-supervised model finetuning pipeline that can further improve the\nperformance of the model. Experiment results show that BSRNN trained only on\nMUSDB18-HQ dataset significantly outperforms several top-ranking models in\nMusic Demixing (MDX) Challenge 2021, and the semi-supervised finetuning stage\nfurther improves the performance on all four instrument tracks.\n", "date": "2022-09-30", "categories": []}
{"id": "2206.08545", "title": "NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling\n  Rates", "abstract": "  Conventionally, audio super-resolution models fixed the initial and the\ntarget sampling rates, which necessitate the model to be trained for each pair\nof sampling rates. We introduce NU-Wave 2, a diffusion model for neural audio\nupsampling that enables the generation of 48 kHz audio signals from inputs of\nvarious sampling rates with a single model. Based on the architecture of\nNU-Wave, NU-Wave 2 uses short-time Fourier convolution (STFC) to generate\nharmonics to resolve the main failure modes of NU-Wave, and incorporates\nbandwidth spectral feature transform (BSFT) to condition the bandwidths of\ninputs in the frequency domain. We experimentally demonstrate that NU-Wave 2\nproduces high-resolution audio regardless of the sampling rate of input while\nrequiring fewer parameters than other models. The official code and the audio\nsamples are available at https://mindslab-ai.github.io/nuwave2.\n", "date": "2022-06-17", "categories": []}
{"id": "2209.11905", "title": "Speech Enhancement with Perceptually-motivated Optimization and Dual\n  Transformations", "abstract": "  To address the monaural speech enhancement problem, numerous research studies\nhave been conducted to enhance speech via operations either in time-domain on\nthe inner-domain learned from the speech mixture or in time--frequency domain\non the fixed full-band short time Fourier transform (STFT) spectrograms. Very\nrecently, a few studies on sub-band based speech enhancement have been\nproposed. By enhancing speech via operations on sub-band spectrograms, those\nstudies demonstrated competitive performances on the benchmark dataset of\nDNS2020. Despite attractive, this new research direction has not been fully\nexplored and there is still room for improvement. As such, in this study, we\ndelve into the latest research direction and propose a sub-band based speech\nenhancement system with perceptually-motivated optimization and dual\ntransformations, called PT-FSE. Specially, our proposed PT-FSE model improves\nits backbone, a full-band and sub-band fusion model, by three efforts. First,\nwe design a frequency transformation module that aims to strengthen the global\nfrequency correlation. Then a temporal transformation is introduced to capture\nlong range temporal contexts. Lastly, a novel loss, with leverage of properties\nof human auditory perception, is proposed to facilitate the model to focus on\nlow frequency enhancement. To validate the effectiveness of our proposed model,\nextensive experiments are conducted on the DNS2020 dataset. Experimental\nresults show that our PT-FSE system achieves substantial improvements over its\nbackbone, but also outperforms the current state-of-the-art while being 27\\%\nsmaller than the SOTA. With average NB-PESQ of 3.57 on the benchmark dataset,\nour system offers the best speech enhancement results reported till date.\n", "date": "2022-09-24", "categories": []}
{"id": "2208.04654", "title": "Extending GCC-PHAT using Shift Equivariant Neural Networks", "abstract": "  Speaker localization using microphone arrays depends on accurate time delay\nestimation techniques. For decades, methods based on the generalized cross\ncorrelation with phase transform (GCC-PHAT) have been widely adopted for this\npurpose. Recently, the GCC-PHAT has also been used to provide input features to\nneural networks in order to remove the effects of noise and reverberation, but\nat the cost of losing theoretical guarantees in noise-free conditions. We\npropose a novel approach to extending the GCC-PHAT, where the received signals\nare filtered using a shift equivariant neural network that preserves the timing\ninformation contained in the signals. By extensive experiments we show that our\nmodel consistently reduces the error of the GCC-PHAT in adverse environments,\nwith guarantees of exact time delay recovery in ideal conditions.\n", "date": "2022-08-09", "categories": []}
{"id": "2208.12387", "title": "Music Separation Enhancement with Generative Modeling", "abstract": "  Despite phenomenal progress in recent years, state-of-the-art music\nseparation systems produce source estimates with significant perceptual\nshortcomings, such as adding extraneous noise or removing harmonics. We propose\na post-processing model (the Make it Sound Good (MSG) post-processor) to\nenhance the output of music source separation systems. We apply our\npost-processing model to state-of-the-art waveform-based and spectrogram-based\nmusic source separators, including a separator unseen by MSG during training.\nOur analysis of the errors produced by source separators shows that waveform\nmodels tend to introduce more high-frequency noise, while spectrogram models\ntend to lose transients and high frequency content. We introduce objective\nmeasures to quantify both kinds of errors and show MSG improves the source\nreconstruction of both kinds of errors. Crowdsourced subjective evaluations\ndemonstrate that human listeners prefer source estimates of bass and drums that\nhave been post-processed by MSG.\n", "date": "2022-08-26", "categories": []}
{"id": "2208.12782", "title": "Mel Spectrogram Inversion with Stable Pitch", "abstract": "  Vocoders are models capable of transforming a low-dimensional spectral\nrepresentation of an audio signal, typically the mel spectrogram, to a\nwaveform. Modern speech generation pipelines use a vocoder as their final\ncomponent. Recent vocoder models developed for speech achieve a high degree of\nrealism, such that it is natural to wonder how they would perform on music\nsignals. Compared to speech, the heterogeneity and structure of the musical\nsound texture offers new challenges. In this work we focus on one specific\nartifact that some vocoder models designed for speech tend to exhibit when\napplied to music: the perceived instability of pitch when synthesizing\nsustained notes. We argue that the characteristic sound of this artifact is due\nto the lack of horizontal phase coherence, which is often the result of using a\ntime-domain target space with a model that is invariant to time-shifts, such as\na convolutional neural network. We propose a new vocoder model that is\nspecifically designed for music. Key to improving the pitch stability is the\nchoice of a shift-invariant target space that consists of the magnitude\nspectrum and the phase gradient. We discuss the reasons that inspired us to\nre-formulate the vocoder task, outline a working example, and evaluate it on\nmusical signals. Our method results in 60% and 10% improved reconstruction of\nsustained notes and chords with respect to existing models, using a novel\nharmonic error metric.\n", "date": "2022-08-26", "categories": []}
{"id": "2208.10886", "title": "A differentiable short-time Fourier transform with respect to the window\n  length", "abstract": "  In this paper, we revisit the use of spectrograms in neural networks, by\nmaking the window length a continuous parameter optimizable by gradient descent\ninstead of an empirically tuned integer-valued hyperparameter. The contribution\nis mostly theoretical at this point, but plugging the modified STFT into any\nexisting neural network is straightforward. We first define a differentiable\nversion of the STFT in the case where local bins centers are fixed and\nindependent of the window length parameter. We then discuss the more difficult\ncase where the window length affects the position and number of bins. We\nillustrate the benefits of this new tool on an estimation and a classification\nproblems, showing it can be of interest not only to neural networks but to any\nSTFT-based signal processing algorithm.\n", "date": "2022-08-23", "categories": []}
{"id": "2208.07994", "title": "Enhancing Audio Perception of Music By AI Picked Room Acoustics", "abstract": "  Every sound that we hear is the result of successive convolutional operations\n(e.g. room acoustics, microphone characteristics, resonant properties of the\ninstrument itself, not to mention characteristics and limitations of the sound\nreproduction system). In this work we seek to determine the best room in which\nto perform a particular piece using AI. Additionally, we use room acoustics as\na way to enhance the perceptual qualities of a given sound. Historically, rooms\n(particularly Churches and concert halls) were designed to host and serve\nspecific musical functions. In some cases the architectural acoustical\nqualities enhanced the music performed there. We try to mimic this, as a first\nstep, by designating room impulse responses that would correlate to producing\nenhanced sound quality for particular music. A convolutional architecture is\nfirst trained to take in an audio sample and mimic the ratings of experts with\nabout 78 % accuracy for various instrument families and notes for perceptual\nqualities. This gives us a scoring function for any audio sample which can rate\nthe perceptual pleasantness of a note automatically. Now, via a library of\nabout 60,000 synthetic impulse responses mimicking all kinds of room,\nmaterials, etc, we use a simple convolution operation, to transform the sound\nas if it was played in a particular room. The perceptual evaluator is used to\nrank the musical sounds, and yield the \"best room or the concert hall\" to play\na sound. As a byproduct it can also use room acoustics to turn a poor quality\nsound into a \"good\" sound.\n", "date": "2022-08-16", "categories": []}
{"id": "2208.08354", "title": "Extract fundamental frequency based on CNN combined with PYIN", "abstract": "  This paper refers to the extraction of multiple fundamental frequencies\n(multiple F0) based on PYIN, an algorithm for extracting the fundamental\nfrequency (F0) of monophonic music, and a trained convolutional neural networks\n(CNN) model, where a pitch salience function of the input signal is produced to\nestimate the multiple F0. The implementation of these two algorithms and their\ncorresponding advantages and disadvantages are discussed in this article.\nAnalysing the different performance of these two methods, PYIN is applied to\nsupplement the F0 extracted from the trained CNN model to combine the\nadvantages of these two algorithms. For evaluation, four pieces played by two\nviolins are used, and the performance of the models are evaluated accoring to\nthe flatness of the F0 curve extracted. The result shows the combined model\noutperforms the original algorithms when extracting F0 from monophonic music\nand polyphonic music.\n", "date": "2022-08-17", "categories": []}
{"id": "2208.07091", "title": "Analysis of impact of emotions on target speech extraction and speech\n  separation", "abstract": "  Recently, the performance of blind speech separation (BSS) and target speech\nextraction (TSE) has greatly progressed. Most works, however, focus on\nrelatively well-controlled conditions using, e.g., read speech. The performance\nmay degrade in more realistic situations. One of the factors causing such\ndegradation may be intrinsic speaker variability, such as emotions, occurring\ncommonly in realistic speech. In this paper, we investigate the influence of\nemotions on TSE and BSS. We create a new test dataset of emotional mixtures for\nthe evaluation of TSE and BSS. This dataset combines LibriSpeech and Ryerson\nAudio-Visual Database of Emotional Speech and Song (RAVDESS). Through\ncontrolled experiments, we can analyze the impact of different emotions on the\nperformance of BSS and TSE. We observe that BSS is relatively robust to\nemotions, while TSE, which requires identifying and extracting the speech of a\ntarget speaker, is much more sensitive to emotions. On comparative speaker\nverification experiments we show that identifying the target speaker may be\nparticularly challenging when dealing with emotional speech. Using our\nfindings, we outline potential future directions that could improve the\nrobustness of BSS and TSE systems toward emotional speech.\n", "date": "2022-08-15", "categories": []}
{"id": "2208.06169", "title": "DDX7: Differentiable FM Synthesis of Musical Instrument Sounds", "abstract": "  FM Synthesis is a well-known algorithm used to generate complex timbre from a\ncompact set of design primitives. Typically featuring a MIDI interface, it is\nusually impractical to control it from an audio source. On the other hand,\nDifferentiable Digital Signal Processing (DDSP) has enabled nuanced audio\nrendering by Deep Neural Networks (DNNs) that learn to control differentiable\nsynthesis layers from arbitrary sound inputs. The training process involves a\ncorpus of audio for supervision, and spectral reconstruction loss functions.\nSuch functions, while being great to match spectral amplitudes, present a lack\nof pitch direction which can hinder the joint optimization of the parameters of\nFM synthesizers. In this paper, we take steps towards enabling continuous\ncontrol of a well-established FM synthesis architecture from an audio input.\nFirstly, we discuss a set of design constraints that ease spectral optimization\nof a differentiable FM synthesizer via a standard reconstruction loss. Next, we\npresent Differentiable DX7 (DDX7), a lightweight architecture for neural FM\nresynthesis of musical instrument sounds in terms of a compact set of\nparameters. We train the model on instrument samples extracted from the URMP\ndataset, and quantitatively demonstrate its comparable audio quality against\nselected benchmarks.\n", "date": "2022-08-12", "categories": []}
{"id": "2208.04462", "title": "Denoising Induction Motor Sounds Using an Autoencoder", "abstract": "  Denoising is the process of removing noise from sound signals while improving\nthe quality and adequacy of the sound signals. Denoising sound has many\napplications in speech processing, sound events classification, and machine\nfailure detection systems. This paper describes a method for creating an\nautoencoder to map noisy machine sounds to clean sounds for denoising purposes.\nThere are several types of noise in sounds, for example, environmental noise\nand generated frequency-dependent noise from signal processing methods. Noise\ngenerated by environmental activities is environmental noise. In the factory,\nenvironmental noise can be created by vehicles, drilling, people working or\ntalking in the survey area, wind, and flowing water. Those noises appear as\nspikes in the sound record. In the scope of this paper, we demonstrate the\nremoval of generated noise with Gaussian distribution and the environmental\nnoise with a specific example of the water sink faucet noise from the induction\nmotor sounds. The proposed method was trained and verified on 49 normal\nfunction sounds and 197 horizontal misalignment fault sounds from the Machinery\nFault Database (MAFAULDA). The mean square error (MSE) was used as the\nassessment criteria to evaluate the similarity between denoised sounds using\nthe proposed autoencoder and the original sounds in the test set. The MSE is\nbelow or equal to 0.14 when denoise both types of noises on 15 testing sounds\nof the normal function category. The MSE is below or equal to 0.15 when\ndenoising 60 testing sounds on the horizontal misalignment fault category. The\nlow MSE shows that both the generated Gaussian noise and the environmental\nnoise were almost removed from the original sounds with the proposed trained\nautoencoder.\n", "date": "2022-08-08", "categories": []}
{"id": "2208.01555", "title": "Low-complexity CNNs for Acoustic Scene Classification", "abstract": "  This technical report describes the SurreyAudioTeam22s submission for DCASE\n2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task\nhas two rules, (a) the ASC framework should have maximum 128K parameters, and\n(b) there should be a maximum of 30 millions multiply-accumulate operations\n(MACs) per inference. In this report, we present low-complexity systems for ASC\nthat follow the rules intended for the task.\n", "date": "2022-08-02", "categories": []}
{"id": "2207.10141", "title": "AudioScopeV2: Audio-Visual Attention Architectures for Calibrated\n  Open-Domain On-Screen Sound Separation", "abstract": "  We introduce AudioScopeV2, a state-of-the-art universal audio-visual\non-screen sound separation system which is capable of learning to separate\nsounds and associate them with on-screen objects by looking at in-the-wild\nvideos. We identify several limitations of previous work on audio-visual\non-screen sound separation, including the coarse resolution of spatio-temporal\nattention, poor convergence of the audio separation model, limited variety in\ntraining and evaluation data, and failure to account for the trade off between\npreservation of on-screen sounds and suppression of off-screen sounds. We\nprovide solutions to all of these issues. Our proposed cross-modal and\nself-attention network architectures capture audio-visual dependencies at a\nfiner resolution over time, and we also propose efficient separable variants\nthat are capable of scaling to longer videos without sacrificing much\nperformance. We also find that pre-training the separation model only on audio\ngreatly improves results. For training and evaluation, we collected new human\nannotations of onscreen sounds from a large database of in-the-wild videos\n(YFCC100M). This new dataset is more diverse and challenging. Finally, we\npropose a calibration procedure that allows exact tuning of on-screen\nreconstruction versus off-screen suppression, which greatly simplifies\ncomparing performance between models with different operating points. Overall,\nour experimental results show marked improvements in on-screen separation\nperformance under much more general conditions than previous methods with\nminimal additional computational complexity.\n", "date": "2022-07-20", "categories": []}
{"id": "2207.04203", "title": "Learning to Separate Voices by Spatial Regions", "abstract": "  We consider the problem of audio voice separation for binaural applications,\nsuch as earphones and hearing aids. While today's neural networks perform\nremarkably well (separating $4+$ sources with 2 microphones) they assume a\nknown or fixed maximum number of sources, K. Moreover, today's models are\ntrained in a supervised manner, using training data synthesized from generic\nsources, environments, and human head shapes.\n  This paper intends to relax both these constraints at the expense of a slight\nalteration in the problem definition. We observe that, when a received mixture\ncontains too many sources, it is still helpful to separate them by region,\ni.e., isolating signal mixtures from each conical sector around the user's\nhead. This requires learning the fine-grained spatial properties of each\nregion, including the signal distortions imposed by a person's head. We propose\na two-stage self-supervised framework in which overheard voices from earphones\nare pre-processed to extract relatively clean personalized signals, which are\nthen used to train a region-wise separation model. Results show promising\nperformance, underscoring the importance of personalization over a generic\nsupervised approach. (audio samples available at our project website:\nhttps://uiuc-earable-computing.github.io/binaural/. We believe this result\ncould help real-world applications in selective hearing, noise cancellation,\nand audio augmented reality.\n", "date": "2022-07-09", "categories": []}
{"id": "2207.05508", "title": "EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use", "abstract": "  In audio classification, differentiable auditory filterbanks with few\nparameters cover the middle ground between hard-coded spectrograms and raw\naudio. LEAF (arXiv:2101.08596), a Gabor-based filterbank combined with\nPer-Channel Energy Normalization (PCEN), has shown promising results, but is\ncomputationally expensive. With inhomogeneous convolution kernel sizes and\nstrides, and by replacing PCEN with better parallelizable operations, we can\nreach similar results more efficiently. In experiments on six audio\nclassification tasks, our frontend matches the accuracy of LEAF at 3% of the\ncost, but both fail to consistently outperform a fixed mel filterbank. The\nquest for learnable audio frontends is not solved.\n", "date": "2022-07-12", "categories": []}
{"id": "2207.01197", "title": "Multi-Modal Multi-Correlation Learning for Audio-Visual Speech\n  Separation", "abstract": "  In this paper we propose a multi-modal multi-correlation learning framework\ntargeting at the task of audio-visual speech separation. Although previous\nefforts have been extensively put on combining audio and visual modalities,\nmost of them solely adopt a straightforward concatenation of audio and visual\nfeatures. To exploit the real useful information behind these two modalities,\nwe define two key correlations which are: (1) identity correlation (between\ntimbre and facial attributes); (2) phonetic correlation (between phoneme and\nlip motion). These two correlations together comprise the complete information,\nwhich shows a certain superiority in separating target speaker's voice\nespecially in some hard cases, such as the same gender or similar content. For\nimplementation, contrastive learning or adversarial training approach is\napplied to maximize these two correlations. Both of them work well, while\nadversarial training shows its advantage by avoiding some limitations of\ncontrastive learning. Compared with previous research, our solution\ndemonstrates clear improvement on experimental metrics without additional\ncomplexity. Further analysis reveals the validity of the proposed architecture\nand its good potential for future extension.\n", "date": "2022-07-04", "categories": []}
{"id": "2206.15423", "title": "Implicit Neural Spatial Filtering for Multichannel Source Separation in\n  the Waveform Domain", "abstract": "  We present a single-stage casual waveform-to-waveform multichannel model that\ncan separate moving sound sources based on their broad spatial locations in a\ndynamic acoustic scene. We divide the scene into two spatial regions\ncontaining, respectively, the target and the interfering sound sources. The\nmodel is trained end-to-end and performs spatial processing implicitly, without\nany components based on traditional processing or use of hand-crafted spatial\nfeatures. We evaluate the proposed model on a real-world dataset and show that\nthe model matches the performance of an oracle beamformer followed by a\nstate-of-the-art single-channel enhancement network.\n", "date": "2022-06-30", "categories": []}
{"id": "2206.14723", "title": "DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With\n  Autoencoding Generative Adversarial Networks", "abstract": "  In contemporary popular music production, drum sound design is commonly\nperformed by cumbersome browsing and processing of pre-recorded samples in\nsound libraries. One can also use specialized synthesis hardware, typically\ncontrolled through low-level, musically meaningless parameters. Today, the\nfield of Deep Learning offers methods to control the synthesis process via\nlearned high-level features and allows generating a wide variety of sounds. In\nthis paper, we present DrumGAN VST, a plugin for synthesizing drum sounds using\na Generative Adversarial Network. DrumGAN VST operates on 44.1 kHz sample-rate\naudio, offers independent and continuous instrument class controls, and\nfeatures an encoding neural network that maps sounds into the GAN's latent\nspace, enabling resynthesis and manipulation of pre-existing drum sounds. We\nprovide numerous sound examples and a demo of the proposed VST plugin.\n", "date": "2022-06-29", "categories": []}
{"id": "2206.12273", "title": "Iterative Sound Source Localization for Unknown Number of Sources", "abstract": "  Sound source localization aims to seek the direction of arrival (DOA) of all\nsound sources from the observed multi-channel audio. For the practical problem\nof unknown number of sources, existing localization algorithms attempt to\npredict a likelihood-based coding (i.e., spatial spectrum) and employ a\npre-determined threshold to detect the source number and corresponding DOA\nvalue. However, these threshold-based algorithms are not stable since they are\nlimited by the careful choice of threshold. To address this problem, we propose\nan iterative sound source localization approach called ISSL, which can\niteratively extract each source's DOA without threshold until the termination\ncriterion is met. Unlike threshold-based algorithms, ISSL designs an active\nsource detector network based on binary classifier to accept residual spatial\nspectrum and decide whether to stop the iteration. By doing so, our ISSL can\ndeal with an arbitrary number of sources, even more than the number of sources\nseen during the training stage. The experimental results show that our ISSL\nachieves significant performance improvements in both DOA estimation and source\nnumber detection compared with the existing threshold-based algorithms.\n", "date": "2022-06-24", "categories": []}
{"id": "2206.06811", "title": "Adversarial Audio Synthesis with Complex-valued Polynomial Networks", "abstract": "  Time-frequency (TF) representations in audio synthesis have been increasingly\nmodeled with real-valued networks. However, overlooking the complex-valued\nnature of TF representations can result in suboptimal performance and require\nadditional modules (e.g., for modeling the phase). To this end, we introduce\ncomplex-valued polynomial networks, called APOLLO, that integrate such\ncomplex-valued representations in a natural way. Concretely, APOLLO captures\nhigh-order correlations of the input elements using high-order tensors as\nscaling parameters. By leveraging standard tensor decompositions, we derive\ndifferent architectures and enable modeling richer correlations. We outline\nsuch architectures and showcase their performance in audio generation across\nfour benchmarks. As a highlight, APOLLO results in $17.5\\%$ improvement over\nadversarial methods and $8.2\\%$ over the state-of-the-art diffusion models on\nSC09 dataset in audio generation. Our models can encourage the systematic\ndesign of other efficient architectures on the complex field.\n", "date": "2022-06-14", "categories": []}
{"id": "2206.09556", "title": "An Empirical Analysis on the Vulnerabilities of End-to-End Speech\n  Segregation Models", "abstract": "  End-to-end learning models have demonstrated a remarkable capability in\nperforming speech segregation. Despite their wide-scope of real-world\napplications, little is known about the mechanisms they employ to group and\nconsequently segregate individual speakers. Knowing that harmonicity is a\ncritical cue for these networks to group sources, in this work, we perform a\nthorough investigation on ConvTasnet and DPT-Net to analyze how they perform a\nharmonic analysis of the input mixture. We perform ablation studies where we\napply low-pass, high-pass, and band-stop filters of varying pass-bands to\nempirically analyze the harmonics most critical for segregation. We also\ninvestigate how these networks decide which output channel to assign to an\nestimated source by introducing discontinuities in synthetic mixtures. We find\nthat end-to-end networks are highly unstable, and perform poorly when\nconfronted with deformations which are imperceptible to humans. Replacing the\nencoder in these networks with a spectrogram leads to lower overall\nperformance, but much higher stability. This work helps us to understand what\ninformation these network rely on for speech segregation, and exposes two\nsources of generalization-errors. It also pinpoints the encoder as the part of\nthe network responsible for these errors, allowing for a redesign with expert\nknowledge or transfer learning.\n", "date": "2022-06-20", "categories": []}
{"id": "2206.07857", "title": "The Scattering Transform Network with Generalized Morse Wavelets and Its\n  Application to Music Genre Classification", "abstract": "  We propose to use the Generalized Morse Wavelets (GMWs) instead of\ncommonly-used Morlet (or Gabor) wavelets in the Scattering Transform Network\n(STN), which we call the GMW-STN, for signal classification problems. The GMWs\nform a parameterized family of truly analytic wavelets while the Morlet\nwavelets are only approximately analytic. The analyticity of underlying wavelet\nfilters in the STN is particularly important for nonstationary oscillatory\nsignals such as music signals because it improves interpretability of the STN\nrepresentations by providing multiscale amplitude and phase (and consequently\nfrequency) information of input signals. We demonstrate the superiority of the\nGMW-STN over the conventional STN in music genre classification using the\nso-called GTZAN database. Moreover, we show the performance improvement of the\nGMW-STN by increasing its number of layers to three over the typical two-layer\nSTN.}\n", "date": "2022-06-16", "categories": []}
{"id": "2206.06057", "title": "Low-complexity deep learning frameworks for acoustic scene\n  classification", "abstract": "  In this report, we presents low-complexity deep learning frameworks for\nacoustic scene classification (ASC). The proposed frameworks can be separated\ninto four main steps: Front-end spectrogram extraction, online data\naugmentation, back-end classification, and late fusion of predicted\nprobabilities. In particular, we initially transform audio recordings into Mel,\nGammatone, and CQT spectrograms. Next, data augmentation methods of Random\nCropping, Specaugment, and Mixup are then applied to generate augmented\nspectrograms before being fed into deep learning based classifiers. Finally, to\nachieve the best performance, we fuse probabilities which obtained from three\nindividual classifiers, which are independently-trained with three type of\nspectrograms. Our experiments conducted on DCASE 2022 Task 1 Development\ndataset have fullfiled the requirement of low-complexity and achieved the best\nclassification accuracy of 60.1%, improving DCASE baseline by 17.2%.\n", "date": "2022-06-13", "categories": []}
{"id": "2606.21335", "title": "Direct Raw Audio Signal Processing via Reservoir Computing: An Investigation into 'Feature-Free' Architectures", "abstract": "This paper evaluates Reservoir Computing (RC) as an autonomous, 'feature-free' framework for audio processing, designed to eliminate traditional, handcrafted feature extraction stages. We investigate whether the high-dimensional temporal dynamics inherent in a reservoir can function as a robust end-to-end processor for the direct classification of raw acoustic signals. By bypassing computationally intensive representations like MFCCs, this approach seeks to mitigate significant intellectual and pre-processing bottlenecks in traditional signal pipelines. Our study evaluates and compares shallow, sequential, and parallel deep reservoir architectures to determine their capacity for hierarchical feature representation. Experimental results demonstrate that the proposed parallel approach consistently outperforms shallow and sequential baselines while maintaining low model complexity. These findings highlight the potential of RC as an efficient and scalable alternative for time-domain audio processing, offering a promising pathway toward deployable, low-power acoustic systems with minimal preprocessing requirements.", "date": "2026-06-19", "categories": []}
{"id": "2606.21635", "title": "Time-Frequency Weighted Losses for Phoneme Reconstruction in DNN-Based Speech Enhancement", "abstract": "Conventional training losses for speech enhancement based on the signal-to-distortion ratio (SDR) treat all time-frequency (TF) regions uniformly, overlooking the fine-grained spectral cues that are relevant to specific phoneme intelligibility. We propose a TF weighting framework that modulates the SDR objective based on local speech presence, speech-to-interference ratio (SIR), and spectral flux. By integrating these factors into a differentiable objective, the framework emphasizes TF bins with high speech-noise competition while also accounting for transient cues such as consonant bursts. Experimental results show that our approach improves objective frequency-weighted enhancement metrics, as well as phoneme recognition accuracy, particularly for consonants. Spectral analysis shows better reconstruction of mid-frequency structures at less adverse SIR.", "date": "2026-06-19", "categories": []}
{"id": "2606.21887", "title": "Improving Engine Sound Analysis in Hot-Test Environments via a RAB-U-Net (Residual Attention Block U-Net) Noise Removal Method", "abstract": "During hot tests on a production line, engine-sound analysis is crucial to ensuring product quality and performance. However, background noise often interferes with accurate sound analysis, leading to potential errors in engine diagnostics. Traditionally, skilled technicians listen to engine sounds to assess engine health, but this is prone to significant inaccuracies. This study presents an innovative deep learning-based approach to address this issue by removing background noise from engine sound recordings using a U-Net neural network structure enhanced with Residual Attention Blocks (RAB-U-Net). Our intelligent noise removal system significantly improves the accuracy of engine noise detection, outperforming traditional techniques and providing a robust solution for real-time applications in production line environments. This study proposes a novel system for engine noise detection in production lines, marking a valuable advancement for the automotive industry in applying deep learning methods to improve the quality of engine diagnostics.", "date": "2026-06-20", "categories": []}
{"id": "2606.22020", "title": "What Do Neural Networks Learn for TDOA Estimation? A Cross-Architecture Probing Study", "abstract": "Neural networks outperform classical GCC-PHAT for Time-Difference-of-Arrival (TDOA) estimation in noise and reverberation, yet their internal strategy remains unexplored. To uncover it, we turn GCC-PHAT's mathematical steps into diagnostic targets, probing hidden layers of three architectures (MLP, CNN, Transformer) and complementing with gradient attribution and causal frequency masking. We find that cross-power computation consistently emerges across all architectures and conditions, while PHAT whitening, the defining step of GCC-PHAT, fails to emerge. Instead, networks learn a magnitude-aware frequency weighting that preserves per-frequency reliability information discarded by PHAT. This makes PHAT an information bottleneck: removing it from both classical and neural GCC pipelines improves performance under additive noise. On real-world reverberant data, PHAT remains the best classical weighting, but end-to-end networks achieve lower error by learning data-adaptive weighting.", "date": "2026-06-20", "categories": []}
{"id": "2606.22952", "title": "Domain-incremental audio classification using domain-specific experts and prototype classifier", "abstract": "This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle is that, the system is unable to access to past or future domain's data at once. We approached domain-incremental learning (DIL) as a frozen-feature replay problem. At each incremental stage, one or two compact experts are trained and then kept fixed; at the final stage, the penultimate features from all frozen experts are concatenated and used to train a lightweight per-class prototype classifier solely on cached features. This design prevents catastrophic forgetting by preserving each expert models at inference. To retain earlier-domain knowledge without storing raw audio, some experts were trained with DeepInversion-based generative replay. A cross-stage regression imputer was trained to fill the expert feature slots that did not yet exist at an ealier stage. We submit four fully DIL-compliant systems: three systems based on diverse frozen five-expert backbones and their cross-stack ensemble achieving 78.15% micro / 77.03% macro on the development set, outperforming every individual backbone on both evaluations.", "date": "2026-06-22", "categories": []}
{"id": "2606.23665", "title": "PHAST-Net: Attention-Guided, Physics-Informed Network for Unified Estimation of Ideal Time-Frequency Representations", "abstract": "We introduce PHAST-Net, an attention-guided, physics-informed network for unified estimation of Ideal Time-Frequency Representations (ITFRs), spanning spectral, tempo-based, metrical, and harmonic representations such as Spectrograms, Tempograms, and Metrograms. PHAST-Net learns an application-general mapping from a constellation of wavelet transforms, the proposed Continuous Log-frequency Adaptive Wavelet Transform (CLAWT), to high-resolution, cross-term-suppressed time-frequency (T-F) representations. The proposed constellation of CLAWTs is selected through Cohen's class kernel analysis to maximise curvature coverage in a logarithmic-frequency T-F plane tailored to harmonic signal structure. PHAST-Net further incorporates a proposed physics-informed auxiliary reprojection loss designed to reconstruct the idealised observed CLAWT constellation from the predicted ITFR and the corresponding Cohen's class kernels during training. This auxiliary objective promotes transform consistency and energy conservation, mitigates pathological target sparsity, and enhances optimisation stability. Attention layers further promote effective cross-term suppression across the input constellation. The log-frequency formulation also enables Harmonic PHAST-Net, which estimates a Harmonic ITFR that isolates fundamental structure, supporting robust fundamental-only representations for speech and music, such as derived fundamental Tempograms and Metrograms. We further introduce Spline-PHAST-Net, which parameterises detected and associated T-F ridges as continuous spline trajectories, enabling arbitrary-grid re-rendering and signal reconstruction. Trained on an effectively unbounded procedurally generated dataset, PHAST-Net demonstrates improved accuracy over established approaches, providing a unified framework for high-resolution, cross-term-robust analysis of speech, music, and broader nonstationary signals.", "date": "2026-06-22", "categories": []}
{"id": "2606.24127", "title": "DTT-BSR+: A Generative-Regression Cascade for Music Source Restoration", "abstract": "Music source restoration (MSR) requires jointly addressing source unmixing and the inversion of non-linear production effects. Current methods struggle to achieve accurate target signal reconstruction while maintaining semantic consistency. To address this limitation, we propose DTT-BSR+, a two-stage cascade MSR system that decouples distribution fitting from signal reconstruction into separate stages. A generative DTT-BSR separator in the first stage produces stems matching the prior of clean sources, and a modified Demucs network in the second stage enhances the first stage output using time-domain and multi-resolution spectral losses. DTT-BSR+ improves multi-mel signal-to-noise ratio (MMSNR) over the single-stage DTT-BSR across all stems, and surpasses the state-of-the-art X-LANCE MSR system on five stems. We also reveal through Fr\\'echet Audio Distance (FAD) decomposition an implicit trade-off between signal reconstruction accuracy and semantic distribution fitting across stems.", "date": "2026-06-23", "categories": []}
{"id": "2606.27320", "title": "Elastic Time: Dynamic Frame Rate Bottlenecks for Neural Audio Coding", "abstract": "Neural audio autoencoders have become a core component of compression, feature extraction, and generation. However, while existing systems support variable bitrate, the vast majority of models still operate at a fixed latent frame-rate, allocating equal temporal budget to regions with very different information density, which can result in unnecessarily long sequences. We introduce Elastic Time, a dynamic frame-rate bottleneck that converts fixed-frame-rate autoencoders to dynamic ones. Our method learns a lightweight latent predictor used to decide which frames can be skipped and later reconstructed, enabling efficient greedy boundary selection at inference. Experiments show our method enables deployment-time rate control while improving efficiency-quality tradeoffs relative to baselines. Overall, we provide a flexible mechanism for adjusting temporal resolution in audio autoencoders, potentially facilitating more efficient downstream modeling for generation and long-context tasks.", "date": "2026-06-25", "categories": []}
{"id": "2606.27751", "title": "From General-Purpose Audio Tagging to Spatially Grounded Sound Event Localization and Detection", "abstract": "This report investigates the extension of pretrained General-Purpose Audio Tagging (GP-AT) models toward spatially grounded Sound Event Localization and Detection (SELD). The proposed AT2SELD framework couples a pretrained AT backbone with compact First-Order Ambisonics (FOA) spatial processing, track-wise SED and Cartesian DOA estimation, permutation aware supervision, and calibration. It characterizes how semantic audio priors support localization-aware scene analysis under data, computation, and deployment constraints. The framework is developed through informed multi-stage Neural Architecture Search (NAS). Stage 1 shows that spectral FOA descriptors, based on magnitude, phase, and Intensity Vectors (IVs), provide the most reliable interface for semantic-to-spatial transfer. Stage 2 identifies early residual spatial encoding as the main capacity-sensitive component, while late track-wise abstraction and recurrent smoothing act mainly as refinement stages. Stage 3 shows that late cross-stitch coupling improves semantic-spatial interaction, whereas early fusion is costlier and less effective. Diagnostic evaluation analyzes the selected architecture under class balancing, focal loss, activity-conditioned DOA supervision, threshold calibration, and transfer across STARSS23, TAU2019, TAU-NIGENS2020, and TAU-NIGENS2021. Focal loss improves the activity point, active-only DOA supervision mitigates inactive target dominance, and validation-selected thresholds recover calibration without replacing spatial learning. Cross-dataset and oracle-activity analyses indicate strong fixed source localization on TAU2019, transferable representations from TAU NIGENS2021, and meaningful but uncertain behavior on STARSS23. Overall, GP-AT priors appear promising for SELD design when embedded in spatial-aware architectures and optimized through integrated calibration and deployment oriented strategies.", "date": "2026-06-26", "categories": []}
{"id": "2606.29575", "title": "TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation", "abstract": "Recent advances in speech separation (SS) have led to compact front-end models with small parameter sizes, yet their high computational cost remains a major barrier for deployment on edge devices. To address this, we propose TF-MoE, a sparse Mixture-of-Experts (MoE) framework that enhances model capacity with almost no increase in inference cost. Our method introduces dynamic expert specialization in time and frequency dimensions through alternating time-wise and frequency-wise MoE modules, each dynamically selecting experts per frame or mel band. Built upon a mel-band-splitting Conformer backbone, TF-MoE achieves strong performance on SS tasks under low-compute settings. Experimental results demonstrate that TF-MoE consistently improves separation performance under computation cost constraints, outperforming BSRNN by +3.8 dB SDR on Libri2Mix with comparable 4.1 GMACs/s inference cost. This positions TF-MoE as a promising candidate for edge-device deployment.", "date": "2026-06-28", "categories": []}
{"id": "2606.31552", "title": "Improving multichannel speech enhancement through accurate room-acoustic simulations", "abstract": "Room-acoustic simulations are widely used to augment training data for deep-learning-based speech enhancement. While most pipelines rely on simplified geometrical acoustics, wave-based approaches offer greater physical accuracy. In this work, we examine how simulation fidelity affects multichannel speech enhancement performance. To this end, we train SpatialNet on datasets augmented with different room-acoustic simulation methods and evaluate the resulting models on measured data. We compare lower-fidelity datasets based on geometrical acoustics with a high-fidelity dataset using advanced acoustic modelling and a hybrid combination of wave-based and geometrical acoustics simulations. Training on the high-fidelity dataset results in an up to 38 % relative reduction in median word error rate compared to the lower-fidelity alternatives. These results show that augmentation with high-fidelity room-acoustic simulations directly translates into improved multichannel speech enhancement performance.", "date": "2026-06-30", "categories": []}