A Deep Learning pipeline that detects and classifies human emotions from speech and audio signals. This project leverages Long Short-Term Memory (LSTM) networks to capture the temporal and sequential dependencies inherent in audio time-series data.
- Feature Extraction: Audio signal processing (e.g., Mel-Frequency Cepstral Coefficients - MFCCs) to extract spectral features from raw audio waves.
- Model: Multi-layer LSTM network designed for sequence classification.
- Domain: Deep Learning, Audio Signal Processing, Affective Computing.
/data- Raw and preprocessed audio files (ignored in git)/notebooks-LSTM_Speech_Audio_Emotion_Detection_and_Classification.ipynbcontaining EDA, feature extraction, and model training./models- Saved.h5model weights (ignored in git)