--- annotations_creators: - machine-generated language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: SpARZ size_categories: - 10K [!NOTE] > **Paper Evaluation Subset:** While this repository contains the full, comprehensively varied dataset, the results reported in the IECON 26 paper were evaluated on a specific subset referred to in the paper as **SpARZ 100**. This corresponds to the simulated room with dimensions **4.9m x 4.9m x 4.9m** at two specific reverberation times: **RT60 = 0.0** and **RT60 = 0.5**. If you wish to reproduce or compare against the paper's exact results on the SpARZ 100 subset, you should use the folders for this specific room setting (e.g., `room_4.9x4.9x4.9m__rt60_0.0__ang_30__snr_5` and `room_4.9x4.9x4.9m__rt60_0.5__ang_30__snr_5`). ### Supported Tasks and Leaderboards - `audio-visual-zooming` - `speech-enhancement` - `source-separation` ### Languages English (`en`) ## Dataset Structure ### How to Use The audio data is distributed as a single `SpARZ.tar` archive to preserve directory structures and speed up downloading. To use the dataset, simply download the `.tar` file and extract it: ```bash tar -xf SpARZ.tar ``` ### Data Instances Each sample in the dataset is structured within a sub-folder that defines the acoustic parameters (e.g., room dimensions, RT60, interference angle, SNR). Inside each sample folder (e.g., `sample_00`), there is a `meta.json` containing the metadata for the audio sample. Example `meta.json`: ```json { "sample_id": 0, "cell_id": 0, "room_dims": [3.0, 4.0, 3.0], "rt60": 0.0, "target_angle_deg": 90, "interferer_angle_deg": 0, "sir_db": 0.0, "snr_db": 0, "target_audio_path": "datasets/LibriSpeech/train-clean-100/302/123504/302-123504-0011.flac", "interferer_audio_path": "datasets/musan/speech/us-gov/speech-us-gov-0210.wav", "clip_duration_s": 5.0, "sample_rate": 16000 } ``` ### Data Fields - `sample_id`: Unique identifier for the sample. - `cell_id`: Spatial cell identifier. - `room_dims`: Dimensions of the simulated room [x, y, z] in meters. - `rt60`: Reverberation time in seconds. - `target_angle_deg`: Angle of the target source in degrees. - `interferer_angle_deg`: Angle of the interfering source in degrees. - `sir_db`: Signal-to-Interference Ratio in dB. - `snr_db`: Signal-to-Noise Ratio in dB. - `target_audio_path`: Path to the original target audio (e.g., LibriSpeech). - `interferer_audio_path`: Path to the original interferer audio (e.g., MUSAN). - `clip_duration_s`: Duration of the audio clip in seconds. - `sample_rate`: Sampling rate of the audio in Hz. ### Data Splits The dataset is provided as a single, unified collection of audio samples. There are no predefined Train, Validation, or Test splits. Researchers can split the dataset based on their specific experimental requirements. ## Dataset Creation ### Curation Rationale The SpARZ dataset was developed to advance research in audio visual zooming and real-time directional speech enhancement by providing highly realistic, parametrically varied acoustic environments. ### Source Data #### Initial Data Collection and Normalization The target speech is sourced from [LibriSpeech](https://www.openslr.org/12) and the interferer noise/speech is sourced from [MUSAN](https://www.openslr.org/17/). These are mixed synthetically using spatial acoustic simulators to generate the final audio. ## Additional Information ### Dataset Curators This dataset was developed as part of a collaborative research effort by the authors of the RDSE framework (Chinmay Deo, Danish Mahajan, Soudeep Ghoshal, and Himanshu Buckchash). ### Licensing Information Creative Commons Attribution 4.0 International (CC-BY-4.0) ### Citation Information If you use this dataset, please cite the corresponding IECON 26 paper: ```bibtex @inproceedings{deo2026rdse, title = {{RDSE}: Real-Time Directional Speech Enhancement Framework for Dual-Mic Commodity Smartphones}, author = {Deo, Chinmay and Mahajan, Danish and Ghoshal, Soudeep and Buckchash, Himanshu}, booktitle = {Proceedings of the 52nd Annual Conference of the IEEE Industrial Electronics Society (IECON)}, address = {Doha, Qatar}, month = oct, year = {2026}, note = {Accepted; to appear. Preprint: arXiv:2610.12476}, } ```