Machine Learning Based Music Categorization (Final Year Academic Project)
Built an ML-based music categorization project using supervised learning. Extracted audio features (e.g., MFCC, chroma, spectral properties) from the GTZAN dataset to support genre classification. Trained and evaluated an SVM model to classify music into 10 genres with reported ~81% accuracy. • Used the GTZAN dataset (10 genres, 1000 audio tracks) • Implemented feature extraction for audio signals • Trained an SVM classifier for multi-class genre prediction • Reported project performance and outcomes
2022 - 2025