Emotion-aware Music Information Retrieval System

Abstract

This study introduces an innovative system designed to refine song recommendations based on the user's expressed emotions. By leveraging a comprehensive database of songs spanning from 1950 to 2019, the system ranks and retrieves top songs, integrating a feedback loop to enhance personalized experiences. It also incorporates the Streamlit platform, enabling interactive input for personalized music recommendations. The objective is to elevate user engagement by prioritizing music selections finely attuned to the user's emotional state, offering an enriched and tailored music recommendation service.