anonymous-submission000/mouse-identity-classification-benchmark

Dataset Card for VocSim - Mouse Identity Classification

Dataset Description

This dataset is used in the VocSim benchmark paper for evaluating the ability of neural audio embeddings to identify individual mice based on their ultrasonic vocalization (USV) syllables. It contains pre-segmented USV syllables from multiple individual mice, derived from recordings created by Van Segbroeck et al. (2017).

The primary task associated with this dataset is supervised classification: training a model to predict the correct mouse identity (speaker field) given an audio input (a single syllable) or its derived features.

Included Files:

• Hugging Face Dataset object containing audio file paths (individual syllables) and metadata (mouse identity).

Dataset Structure

Data Instances

A typical example in the dataset looks like this:

{
  'audio': {'path': '/path/to/datasets/mouse_identity/BM003/BM003_syllable_1.wav', 'array': array([...], dtype=float32), 'sampling_rate': 250000},
  'subset': 'mouse_identity',
  'index': 50,
  'speaker': 'BM003', # The crucial individual mouse ID (target label)
  'label': 'BM003_syllable_1', # Syllable-specific identifier
  'original_name': 'BM003/BM003_syllable_1.wav' # Example original relative path
}

Citation Information

If you use this dataset, please cite the VocSim benchmark paper, and the MUPET software if relying on the provided segmentation:

@unpublished{vocsim2025,
  title={VocSim: A Training-Free Benchmark for Content Identity in Single-Source Audio Embeddings},
  author={Anonymous},
  year={2025},
  note={Submitted manuscript}
}

@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}
}