---
configs:
  - config_name: default
    data_files:
      - split: test
        path: meta_data.jsonl
license:
  - cc-by-nc-sa-4.0
  - cc-by-4.0
  - cc-by-nc-3.0
  - cc-by-nc-4.0
  - odc-by
  - mit
task_categories:
  - audio-classification
  - question-answering
language:
  - en
tags:
  - machine hearing
  - audio
  - time series
  - timeseries
  - benchmark
  - classification
  - scientific signals
  - multimodal llm
size_categories:
  - 1K<n<10K
pretty_name: 'Machine Hearing Benchmark (MHB)'
---

# Machine Hearing Benchmark (MHB)

<p align="center">
  <img src="process/MHB.png" alt="Overview of MHB across seven domains and 13 tasks" width="700">
</p>

This repository provides the **Machine Hearing Benchmark (MHB)** introduced in [**Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception?**](https://arxiv.org/abs/2611.10693)

Multimodal LLMs have made substantial progress in perceiving the physical world, but their capabilities remain largely centered on signals that humans can directly see or hear. The physical world contains waves, vibrations, and oscillations far beyond human auditory perception. MHB investigates whether Audio Large Language Models (ALLMs) can generalize to this broader form of *machine hearing*.

MHB comprises **6,128 instances across 13 classification tasks and seven domains: Astronomy, Earth Science, Healthcare, Electromagnetic, Underwater Acoustics, Bioacoustics, and Manufacturing**. It spans highly heterogeneous input regimes, with sampling rates from approximately 10⁻⁵ to 10⁶ Hz, sequence lengths from 10¹ to 10⁶ samples, and both single- and multi-channel inputs. Each instance consists of an input, a task-specific textual prompt, and a predefined label set.

# Dataset Licenses and Distribution

MHB integrates data from multiple sources. The following table lists the source or processed-release licenses and whether the corresponding input data are included in this repository.

| MHB Task | Data Source | Source / Release License | Input Files | Reference |
| --- | --- | --- | --- | --- |
| GWOSC | GWOSC_GW_Event | <div align="center">CC BY-NC-SA 4.0 (SciTS)</div> | <div align="center">Included</div> | <div align="center"><a href="https://huggingface.co/datasets/OpenTSLab/SciTS">SciTS</a></div> |
| LEAVES | LEAVES | <div align="center">CC BY 4.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://nadc.china-vo.org/res/r100962/?lang=en">NADC</a></div> |
| Iquique | Iquique | <div align="center">Not specified</div> | <div align="center"><strong>Reconstruction required</strong></div> | <div align="center"><a href="https://seisbench.readthedocs.io/en/main/pages/data/benchmark_datasets.html#iquique">SeisBench</a></div> |
| Llaima | Llaima | <div align="center">CC BY-NC 3.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://data.mendeley.com/datasets/dv8nwdd36k/1">Mendeley Data</a></div> |
| Yaseen | Yaseen | <div align="center">Not specified</div> | <div align="center"><strong>Reconstruction required</strong></div> | <div align="center"><a href="https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-">Official GitHub</a></div> |
| CirCor_loc | CirCor | <div align="center">ODC-BY 1.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://physionet.org/content/circor-heart-sound/1.0.3/">PhysioNet</a></div> |
| RadSeg | RadSeg | <div align="center">MIT</div> | <div align="center">Included</div> | <div align="center"><a href="https://github.com/abcxyzi/radseg">RadSeg</a></div> |
| RadioML | RadioML2016.10A | <div align="center">CC BY-NC-SA 4.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://www.deepsig.ai/datasets/">DeepSig</a></div> |
| ShipsEar | ShipsEar | <div align="center">Not specified</div> | <div align="center"><strong>Reconstruction required</strong></div> | <div align="center"><a href="https://underwaternoise.atlanttic.uvigo.es/">Official database</a></div> |
| DeepShip | DeepShip | <div align="center">Not specified</div> | <div align="center"><strong>Reconstruction required</strong></div> | <div align="center"><a href="https://github.com/irfankamboh/DeepShip">Official GitHub</a></div> |
| MarmAudio | MarmAudio | <div align="center">CC BY 4.0</div> | <div align="center">Included</div> | <div align="center"><a href="https://zenodo.org/records/15017207">Zenodo</a></div> |
| SDUST_bear | `sdust_bearing` via RMIS | <div align="center">MIT<br>(RMIS processed release)</div> | <div align="center">Included</div> | <div align="center"><a href="https://huggingface.co/datasets/jiangab/RMIS">RMIS</a></div> |
| PU_cur | `pu_cur` via RMIS | <div align="center">MIT<br>(RMIS processed release)</div> | <div align="center">Included</div> | <div align="center"><a href="https://huggingface.co/datasets/jiangab/RMIS">RMIS</a></div> |

**License notes.** MHB's original benchmark contributions, to the extent the maintainers hold the relevant rights, are released under **CC BY-NC-SA 4.0**. Original data sources and processed releases retain their own applicable conditions and attribution requirements. No waveform files from Iquique, Yaseen, ShipsEar, or DeepShip are redistributed here; their metadata and reconstruction scripts are provided instead.

# Dataset Structure

The repository contains a unified `meta_data.jsonl` file, task-specific data folders, and processing scripts. Each task folder follows the naming convention `Domain-Task-Task_Name`, with input data stored under `raw_input/`.

```text
├── README.md
├── meta_data.jsonl
├── process/
│   ├── MHB.png
│   ├── map_deepship.py
│   ├── map_iquique.py
│   ├── map_shipsear.py
│   ├── map_yaseen.py
│   └── process_raw2wav.py
├── Astronomy-GWOSC-Gravitational_Wave_Event_Detection/
│   ├── raw_input/
│   └── wav_input/                  # generated on demand
├── Astronomy-LEAVES-Light_Curve_Classification/
│   └── raw_input/
├── ...
└── Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis/
    └── raw_input/

# wav_data.jsonl is generated at the repository root.
```

- **`meta_data.jsonl`**: The complete set of 6,128 instances and annotations, including entries for the four datasets that must be obtained externally.
- **`<Dataset_Folder>/raw_input/`**: Input files in their original formats (`csv`, `npy`, `wav`, or `flac`). Directly hosted tasks contain their files; the four externally sourced tasks are reconstructed locally.
- **`process/map_*.py`**: Scripts for selecting and mapping externally obtained data to the exact names and relative paths in `meta_data.jsonl`.
- **`process/process_raw2wav.py`**: Optional conversion of six non-WAV tasks to `wav_input/`, also generating `wav_data.jsonl`.

The four externally hosted datasets can be reconstructed locally using the corresponding mapping scripts. The `wav_input/` directories and `wav_data.jsonl` are generated on demand by `process/process_raw2wav.py`.

# Tasks

All 13 tasks in MHB are formulated as classification tasks, with detailed task definitions provided in [Appendix A.1 of the paper](https://arxiv.org/abs/2611.10693).

| Domain | Task | Instances | Classification Objective | Dataset Folder |
| --- | --- | --- | --- | --- |
| Astronomy | GWOSC | 512 | Gravitational-wave event detection | `Astronomy-GWOSC-Gravitational_Wave_Event_Detection` |
| Astronomy | LEAVES | 1,024 | Variable-star classification from light curves | `Astronomy-LEAVES-Light_Curve_Classification` |
| Earth Science | Iquique | 502 | Earthquake focal-depth classification | `Earth_Science-Iquique-Earthquake_Focal_Depth_Classification` |
| Earth Science | Llaima | 358 | Volcanic seismic-event classification | `Earth_Science-Llaima-Volcanic_Seismic_Event_Classification` |
| Healthcare | Yaseen | 200 | Heart-sound classification | `Healthcare-Yaseen-Heart_Sound_Classification` |
| Healthcare | CirCor_loc | 697 | Auscultation-location identification from PCG recordings | `Healthcare-CirCor_loc-Auscultation_Location_Identification` |
| Electromagnetic | RadSeg | 482 | Radar waveform/coding-scheme classification | `Electromagnetic-RadSeg-Coding_Scheme-Classification` |
| Electromagnetic | RadioML | 550 | Radio modulation classification | `Electromagnetic-RadioML-Radio_Modulation_Classification` |
| Underwater Acoustics | ShipsEar | 84 | Vessel and sound-source classification | `Underwater_Acoustics-ShipsEar-Vessel_And_Sound_Source_Classification` |
| Underwater Acoustics | DeepShip | 527 | Vessel-type classification | `Underwater_Acoustics-DeepShip-Vessel_Type_Classification` |
| Bioacoustics | MarmAudio | 300 | Marmoset vocalization-type classification | `Bioacoustics-MarmAudio-Marmoset_Vocalisation-Classification` |
| Manufacturing | SDUST_bear | 572 | Bearing fault diagnosis from vibration signals | `Manufacturing-SDUST_bear-Vibration_Fault_Diagnosis` |
| Manufacturing | PU_cur | 320 | Bearing fault diagnosis from motor-current signals | `Manufacturing-PU_cur-Motor_Current_Fault_Diagnosis` |
| **Total** | **13 tasks** | **6,128** | | |

# `meta_data.jsonl` Format

Each line is a JSON object representing one instance. The following is a shortened example; the text fields are illustrative:

```json
{
  "task_name": "GWOSC",
  "dataset_name": "GWOSC_GW_Event",
  "id": "GW_3413075",
  "data_type": "csv",
  "input_ts": {
    "num_channel": 1,
    "channel_detail": [],
    "path": "Astronomy-GWOSC-Gravitational_Wave_Event_Detection/raw_input/0003747_gn_m1_14_87_m2_3_63_mtotal_18_q_4_1.csv",
    "length": 64000,
    "timestamps": [],
    "fs": 16000
  },
  "input_text": "Task-specific classification instruction",
  "gt_text": "The classification result is: normal",
  "gt_result": {
    "class_list": ["normal", "anomaly"],
    "gt_class": ["normal"]
  },
  "meta_data": {}
}
```

| Field | Description |
| --- | --- |
| `task_name` | MHB task identifier (e.g., `GWOSC`, `CirCor_loc`, `SDUST_bear`). |
| `dataset_name` | Original or processed-source dataset identifier. |
| `id` | Unique identifier of the instance. |
| `data_type` | Input file format: `csv`, `npy`, `wav`, or `flac`. |
| `input_ts.path` | Path to the input file, **relative to the MHB repository root**. For externally sourced datasets, the corresponding mapping script restores this file locally. |
| `input_ts.length` | Input sequence length in time steps. |
| `input_ts.num_channel` | Number of input channels. |
| `input_ts.channel_detail` | Optional channel descriptions; empty when not provided. |
| `input_ts.fs` | Original sampling rate in Hz. |
| `input_ts.timestamps` | Optional timestamp information, usually an empty list. |
| `input_text` | Task-specific textual instruction given to the model. |
| `gt_text` | Reference answer in text form. |
| `gt_result.class_list` | List of valid classification labels. |
| `gt_result.gt_class` | Ground-truth class label as a list. |
| `meta_data` | Source-specific metadata (e.g., `trace_name` for Iquique or `ori_file_path` and `seg_index` for ShipsEar); fields vary by task. |

Paths in `meta_data.jsonl` are relative to the repository root. For example, input data can be loaded using `record["input_ts"]["path"]` relative to the repository root.

For models that generate additional explanations or text, users may append an output-format constraint to the end of `input_text` during inference, such as:

```text
"Output ONLY the classification result. Do not output any other text, explanation, or punctuation."
```

# Reconstructing Externally Hosted Datasets

The input data for **Iquique, Yaseen, ShipsEar, and DeepShip** are not bundled in this release. Download the source data from the linked official providers, then run the relevant script from the **MHB repository root**. Each script uses `meta_data.jsonl` to select the exact MHB subset, creates the required task folder and `raw_input/` directory, and preserves the relative paths already defined in the metadata. No changes to `meta_data.jsonl` are needed.

For the MHB mapping scripts, install the necessary Python packages:

```bash
pip install numpy h5py soundfile
```

## Iquique (502 instances)

**Source:** [SeisBench — Iquique](https://seisbench.readthedocs.io/en/main/pages/data/benchmark_datasets.html#iquique).

Obtain the SeisBench Iquique dataset and locate the folder containing **`waveforms.hdf5`** and **`metadata.csv`**. Run:

```bash
python process/map_iquique.py /path/to/Iquique
```

The script uses `meta_data.trace_name` to extract the specified three-component seismic traces from the HDF5 file and saves 502 NPY files with the names and paths defined in `meta_data.jsonl`.

## Yaseen (200 instances)

**Source:** [Official Yaseen GitHub repository](https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-).

Download or clone the repository and extract the five category archives (`AS`, `MR`, `MS`, `MVP`, and `N`). The extracted directories are named `AS_New_3주기/`, `MR_New_3주기/`, `MS_New_3주기/`, `MVP_New_3주기/`, and `N_New_3주기/`. For example, from the downloaded source directory:

```bash
for archive in *_New_3주기.rar; do unrar x "$archive"; done
```

Use **`unrar` or a recent compatible RAR extractor**. Then, from the MHB root:

```bash
python process/map_yaseen.py /path/to/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-
```

The script selects and copies the 200 specified WAV files from the five categories.

## ShipsEar (84 instances)

**Original data:** [ShipsEar official website](https://underwaternoise.atlanttic.uvigo.es/) (access to the complete dataset may require contacting its authors).

**Train/test split:** [ShipsEar — An Unofficial Train-Test Split](https://github.com/YuanX9/ShipsEar-An-Unofficial-Train-Test-Split), using its **12-class split**.

Download ShipsEar and apply the linked repository's 12-class `train-test-split.py` to produce a folder such as:

```text
shipsear_with_split-12class/
├── train/
└── test/
```

Then run:

```bash
python process/map_shipsear.py /path/to/shipsear_with_split-12class/test
```

Following the segmentation protocol associated with the referenced **12-class split**, the mapping script extracts **30-second windows at 15-second intervals**, including the designated Trawler test excerpt, and writes the 84 MHB WAV files to their specified `raw_input/` paths.

## DeepShip (527 instances)

**Original data:** [Official DeepShip GitHub](https://github.com/irfankamboh/DeepShip).

**Train/test split:** [Method for Splitting the DeepShip Dataset](https://github.com/Noxo02/Method-for-Splitting-the-DeepShip-Dataset).

Download DeepShip and apply the linked repository's `train.txt`, `test.txt`, and `code.ipynb` to obtain the segmented test data. The split code generates a directory named **`DeepShip_test`**, with the following structure:

```text
DeepShip_test/
├── Cargo/
├── Passengership/
├── Tanker/
│   └── 20170822-133/
│       └── 172736.wav/
│           └── 00027.wav
└── Tug/
```

Pass the path to the `DeepShip_test` **directory** (the one containing the four category folders):

```bash
python process/map_deepship.py /path/to/DeepShip_test
```

The script selects the 527 designated three-second test segments, renames each output WAV using the sample `id`, and copies the files to the corresponding `raw_input/` location.

# RAW-to-WAV Conversion

For Audio LLM APIs that only accept WAV files, MHB provides `process/process_raw2wav.py`. **First reconstruct Iquique**, because its NPY inputs are not included in the repository. From the MHB root, run:

```bash
pip install numpy pandas
python process/process_raw2wav.py
```

The script converts **GWOSC, LEAVES, Iquique, Llaima, RadSeg, and RadioML** (3,428 instances) into each task's `wav_input/` directory.

The script also generates `wav_data.jsonl` in the MHB root, containing **all 6,128 instances**. For the six converted tasks, it updates `data_type` and `input_ts.path`, `length`, `num_channel`, and `fs`; it additionally sets `input_ts.channel_detail` to `[]` for **Iquique, RadSeg, and RadioML**. The other seven tasks remain unchanged and retain their original WAV/FLAC input references where applicable.

# Citation

If you use MHB, please cite:

```bibtex
@misc{liu2026machinehearing,
  title         = {Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception?},
  author        = {Liwei Liu and Jun Tao and Xingyuan Li and Xuenan Xu and Chen Zhang and Junyi Wang and Boya Zhang and Xiaoyu Yang and Zhen-Hua Ling and Wen Wu and Chao Zhang},
  year          = {2026},
  eprint        = {2611.10693},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SD},
  url           = {https://arxiv.org/abs/2611.10693}
}
```

Please also cite the original dataset publications and the relevant processing or split releases, including [SciTS](https://huggingface.co/datasets/OpenTSLab/SciTS), [RMIS/FISHER](https://huggingface.co/datasets/jiangab/RMIS), and the referenced [ShipsEar](https://github.com/YuanX9/ShipsEar-An-Unofficial-Train-Test-Split) / [DeepShip](https://github.com/Noxo02/Method-for-Splitting-the-DeepShip-Dataset) split methods, as applicable.