Music Recommended Systems using Machine Learning Approach

Abstract: This work introduces a comprehensive music recommendation system that harnesses the power of artificial intelligence (AI) to understand and cater to human emotions. The system begins by curating a vast dataset comprising songs annotated with emotional attributes, meticulously collected from various sources. Leveraging advanced machine learning techniques, including sentiment analysis and feature extraction from audio signals, the system trains models to discern the nuanced emotional dimensions embedded within music. Through an intuitive user interface, individuals interact by either expressing their current emotional state or selecting from predefined emotional categories. Subsequently, the system utilizes this input to generate tailored music recommendations, ensuring that the suggested tracks resonate harmoniously with the user's mood. An iterative feedback loop allows users to rate there commendations, fostering continuous refinement and improvement of the recommendation algorithms. The system& deployment as a user-friendly application empowers individuals to effortlessly discover music that not only entertains but also resonates deeply with their emotional landscape, enhancing their overall listening experience. This work represents a significant advancement in personalized music recommendation systems, bridging the gap between AI technology and human emotion in the realm of music discovery and enjoyment.

Keywords: Machine learning, Music recommendation, MIDI, Multimodal fusion, Feature Extraction, Filtering Techniques