Update README.md

README.md

@@ -27,3 +27,52 @@ configs:
   - 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 used in **Goffinet et al. (2021)~\cite{goffinet2021low}**.
+
+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+@unpublished{vocsim2025,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}
+}
+```