🐦 Bird Song Classification with Deep Learning

Overview

Advanced machine learning project for automated bird species identification using audio signal processing and neural networks.

🚀 Key Achievements

• 98.89% test accuracy for bird species classification
• Developed two state-of-the-art neural network models
• Successfully classified 5 distinct bird species

🧠 Technical Stack

• Languages: Python
• Libraries: PyTorch, Pandas, GeoPandas
• Techniques: MFCC Feature Extraction, CNN, LSTM

🏆 Model Performance

Model	Validation Accuracy	Test Accuracy	Key Strength
LSTM	93.54%	98.89%	Temporal Pattern Recognition
CNN	93.77%	96.98%	Quick Feature Extraction

📊 Dataset

• 5 Bird Species Classified
• 5,422 Audio Recordings
• Geospatially Mapped Observations

🔬 Technical Approach

• Mel-Frequency Cepstral Coefficients (MFCC) Feature Extraction
• Bidirectional LSTM Temporal Learning
• Convolutional Neural Network Feature Mapping

💡 Potential Applications

• Ecological Research
• Biodiversity Monitoring
• Automated Wildlife Sound Classification

🚀 Future Work

• Expand to more bird species
• Real-time audio classification
• Enhanced model architectures