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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-*
---
# 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:
```python
{
'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@unpublished{vocsim2025,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: Zero-Shot Audio Similarity Benchmark for Neural 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}
}
``` |