Datasets:

vocsim
/

mouse-strain-classification-benchmark

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

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

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

{
  "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

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 — 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

@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}
}