Datasets:
Maris Basha
De-anonymize and polish README: add ICML 2026 paper info, arXiv DOI, cross-links to vocsim/* repos
4597073 | dataset_info: | |
| features: | |
| - name: audio | |
| dtype: | |
| audio: | |
| sampling_rate: 250000 | |
| - name: label | |
| dtype: string | |
| - name: subset | |
| dtype: string | |
| - name: index | |
| dtype: int64 | |
| - name: speaker | |
| dtype: string | |
| - name: original_name | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 2083205598.525 | |
| num_examples: 31475 | |
| download_size: 1250386212 | |
| dataset_size: 2083205598.525 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: cc-by-4.0 | |
| tags: | |
| - audio | |
| - animal-vocalization | |
| - ultrasonic-vocalization | |
| - mouse | |
| - bioacoustics | |
| - classification | |
| - benchmark | |
| - vocsim | |
| size_categories: | |
| - 10K<n<100K | |
| pretty_name: VocSim — Mouse Strain Classification | |
| # VocSim — Mouse Strain Classification | |
| [](https://github.com/vocsim/benchmark) | |
| [](https://huggingface.co/datasets/vocsim/public) | |
| [](https://creativecommons.org/licenses/by/4.0/) | |
| A companion dataset for the **VocSim** benchmark that tests whether audio embeddings preserve **strain identity** in mouse ultrasonic vocalizations (USVs). It contains pre-segmented USV syllables from **C57BL/6J (C57)** and **DBA/2J (DBA)** mice, sampled at the native 250 kHz so high-frequency structure is preserved. | |
| > Basha, M., Zai, A. T., Stoll, S., & Hahnloser, R. H. R. *VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio.* ICML 2026. [arXiv:2512.10120](https://doi.org/10.48550/arXiv.2512.10120) | |
| ## Task | |
| Supervised binary classification: given an audio syllable (or features derived from it), predict the correct strain (`label` ∈ {`C57`, `DBA`}). In the paper we use this dataset to validate that VocSim-top embeddings transfer to a downstream bioacoustic task. | |
| ## Schema | |
| ```python | |
| { | |
| "audio": {"array": np.ndarray, "sampling_rate": 250000}, | |
| "subset": "mouse_strain", | |
| "index": 101, | |
| "speaker": "C57_file_001", | |
| "label": "C57", # target: C57 or DBA | |
| "original_name": "C57/C57_file_001.wav" | |
| } | |
| ``` | |
| ## Quick start | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("vocsim/mouse-strain-classification-benchmark", split="train") | |
| print(ds[0]) | |
| ``` | |
| For end-to-end evaluation, use [github.com/vocsim/benchmark](https://github.com/vocsim/benchmark) — see `reproducibility/scripts/mouse_strain.py`. | |
| ## Source data | |
| USV recordings and segmentation rely on MUPET (Van Segbroeck et al., 2017). Please cite both that work and the VocSim paper if you use this dataset. | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{basha2026vocsim, | |
| title = {VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio}, | |
| author = {Basha, Maris and Zai, Anja T. and Stoll, Sabine and Hahnloser, Richard H. R.}, | |
| booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)}, | |
| year = {2026}, | |
| doi = {10.48550/arXiv.2512.10120} | |
| } | |
| @article{VanSegbroeck2017, | |
| author = {Van Segbroeck, Maarten and Knoll, Aaron T. and Levitt, Patricia and Narayanan, Shrikanth}, | |
| title = {{MUPET}-Mouse Ultrasonic Profile ExTraction: A Signal Processing Tool for Rapid and Unsupervised Analysis of Ultrasonic Vocalizations}, | |
| journal = {Neuron}, | |
| volume = {94}, | |
| number = {3}, | |
| pages = {465--485.e5}, | |
| year = {2017}, | |
| doi = {10.1016/j.neuron.2017.04.018} | |
| } | |
| ``` | |