sensein/Mouse_ProSAP1_Shank2_Male_Female_Oestrus_Interaction

Mouse USV Detector - ProSAP1/Shank2 Male-Oestrus Female Interactions

Deep learning model for detecting ultrasonic vocalizations (USVs) in mouse recordings from male-oestrus female social interactions.

Model Details

• Model Type: Convolutional Neural Network (CNN)
• Task: Binary classification (USV vs. noise)
• Architecture: 4-layer CNN with batch normalization and dropout
• Parameters: 10.4M
• Framework: PyTorch 2.0+

Performance

• Validation Accuracy: 96.0%
• Noise Detection: 94.7%
• USV Detection: 98.0%

Training Protocol

• Subject: S2-4-65
• Strain: ProSAP1/Shank2
• Behavior: Male-oestrus female interactions (10 min + 3 min)
• Dataset: 7,188 training samples, 2,146 validation samples
• Epochs: 10

Audio Specifications

• Sample Rate: 250 kHz (ultrasonic)
• USV Frequency Range: 40-100 kHz
• Input Format: 64x64 spectrogram patches

Usage

import torch
from model_architecture import load_model
from inference_example import predict

# Load model
model = load_model('final_usv_model.pth')

# Predict on audio file
result = predict('audio.wav', model)
print(f"USV: {result['is_usv']}, Confidence: {result['confidence']:.2%}")

Requirements

pip install torch numpy librosa scipy

Files

• final_usv_model.pth - Trained model weights (41.9 MB)
• model_architecture.py - CNN architecture definition
• inference_example.py - Example inference code
• config.json - Model configuration and metadata
• requirements.txt - Python dependencies

Citation

If you use this model, please cite:

@misc{usv_detector_prosap1_shank2,
  title={Mouse USV Detector for ProSAP1/Shank2 Social Interactions},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/your-username/model-name}}
}

License

[Specify your license here]

Methodology

Based on the DeepSqueak methodology for USV detection:

• Spectrogram-based feature extraction
• Tonality calculation for USV identification
• Automated detection with manual validation
• Deep learning classification for robust detection

Contact

[Your contact information]