Datasets:
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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}
}
```
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