Music Genre Classifier and Recommender

Reclassification of music genres plays an important role in organizations and is a recommendation for audio content in large music collections. Traditionally, manual listening and tagging was required for this genre classification, which was time-consuming and subjective. To improve this, we propose an automated system that categorizes music genres and provides personalized recommendations based on user preferences. Our system extracts related features from audio files and uses machine learning models to predict the corresponding music genre. Additionally, the categorized genres are integrated into recommended frames suggesting similar tracks and related events for users. The flask-based web interface supports user registration, admin registration, uploading audio files, and visualizing recommendations. This project works efficiently with commodity hardware and uses CSV and MySQL databases for data management. This approach highlights the user-friendly, scalability, and application possibilities for real-world application, particularly for music streaming platforms and event recommendation services. Experimental results provide accurate genre classification and user-specific recommendations. This improves the typical user experience.