Datasets:
Maris Basha
De-anonymize and polish README: add ICML 2026 paper info, arXiv DOI, cross-links to vocsim/* repos
0d591ef | dataset_info: | |
| features: | |
| - name: audio | |
| dtype: | |
| audio: | |
| sampling_rate: 250000 | |
| - name: speaker | |
| dtype: string | |
| - name: subset | |
| dtype: string | |
| - name: index | |
| dtype: int64 | |
| - name: label | |
| dtype: string | |
| - name: original_name | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 12703856316.896 | |
| num_examples: 99024 | |
| download_size: 8163587149 | |
| dataset_size: 12703856316.896 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: cc-by-4.0 | |
| tags: | |
| - audio | |
| - animal-vocalization | |
| - ultrasonic-vocalization | |
| - mouse | |
| - bioacoustics | |
| - speaker-identification | |
| - benchmark | |
| - vocsim | |
| size_categories: | |
| - 10K<n<100K | |
| pretty_name: VocSim — Mouse Identity Classification | |
| # VocSim — Mouse Identity 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 **individual identity** in mouse ultrasonic vocalizations (USVs). It contains pre-segmented USV syllables from multiple individual mice (the `speaker` field), sampled at the native 250 kHz, derived from recordings by Van Segbroeck et al. (2017). | |
| > 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 multi-class classification: given an audio syllable (or features derived from it), predict which individual mouse produced it. The target is `speaker`. In the paper we use this dataset to validate that VocSim-top embeddings transfer to a fine-grained downstream bioacoustic task. | |
| ## Schema | |
| ```python | |
| { | |
| "audio": {"array": np.ndarray, "sampling_rate": 250000}, | |
| "subset": "mouse_identity", | |
| "index": 50, | |
| "speaker": "BM003", # target: individual mouse ID | |
| "label": "BM003_syllable_1", # syllable-specific identifier | |
| "original_name": "BM003/BM003_syllable_1.wav" | |
| } | |
| ``` | |
| ## Quick start | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("vocsim/mouse-identity-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_identity.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} | |
| } | |
| ``` | |