Music Genre Classifier Using Deep Learning

Traditional approaches to music genre classification, such as manual tagging, metadata-based categorization, and shallow machine learning models, remain limited in accuracy, scalability, and adaptability to diverse datasets. To overcome these challenges, modern analytical techniques integrate audio signal processing with deep learning frameworks. The proposed system combines feature extraction methods—such as Mel-Frequency Cepstral Coefficients (MFCCs), Chroma, and Spectral analysis—with Recurrent Neural Networks (RNNs) to effectively capture the sequential and temporal characteristics of music. While feature extraction isolates critical elements such as timbre, rhythm, and harmony, RNNs learn time-dependent patterns across frames, enabling selective identification of genres. This integration enables automated predictions with improved accuracy, faster processing, greater reproducibility, and scalability for real-world applications. Designed as a closed pipeline, the system ensures robust preprocessing, efficient classification, and user-friendly output. Recent advances in deep learning–based audio analysis have significantly expanded applications in multimedia, particularly music streaming, recommendation systems, and digital libraries. This project presents the design and implementation of a web-based Music Genre Classifier, highlighting its effectiveness in automated classification, playlist generation, and music information retrieval.