Deep Learning Music Analysis and Classification

AI-Powered Audio Analysis

This project focuses on accurately classifying songs into their respective genres using advanced Deep Learning and Audio Signal Processing techniques. It combines cutting-edge technology and creativity to deliver reliable music categorization, ideal for applications in music streaming, curation, and recommendation systems.

Key Highlights:

Audio Feature Extraction: Leveraged tools like Librosa to extract key audio features such as Mel spectrograms, MFCCs, and spectral contrast.
Deep Learning Models: Implemented CNNs ,RNNs and LSTMs to capture both spatial and sequential data in audio signals.
Multi-Genre Classification: Achieved high accuracy across multiple genres, ensuring scalability for large datasets.
Custom Pipelines: Built robust pre-processing pipelines for audio cleaning, normalization, and augmentation to enhance model performance.
Real-World Applications: Can be adapted for music discovery platforms, personalized playlists, and artist trend analysis.