Smart Music Suggestion System Using Real Time Emotion Recognition

Abstract

In today’s digital landscape, music profoundly influences human emotions,
offering comfort, motivation, and a sense of connection. Yet, the vast volume of available tracks
can overwhelm users when seeking music that aligns with their current emotional state. This
paper presents Music Mood, a multi-modal music recommendation system designed to enhance
the listening experience by leveraging advanced machine learning techniques beyond traditional
natural language processing approaches. The proposed system integrates chat-based text
analysis, vocal tone recognition, and facial emotion detection to accurately infer users’ emotional
states. By synthesizing these modalities, Music Mood generates personalized music
recommendations that closely correspond to the user’s mood and preferences. The primary goal
of this framework is to achieve a nuanced understanding of emotional context while reliably
predicting individual musical inclinations. Through the fusion of textual, auditory, and visual
emotion cues, Music Mood provides a holistic, adaptive, and emotionally intelligent
recommendation system, delivering a more immersive and authentic music listening experience