Real- Time Emotion-Based Music Recommendation System Using YOLOv5 and Spotify API

Music is a universal language that evokes emotions and influences mood. Recent advances in affective computing and multimodal fusion have enabled innovative music recommendation systems. This work explores the relationship between facial expressions and music preferences, proposing a novel music recommendation system. A system is developed that captures and interprets users' emotional states with high accuracy,achieving high mAP (mean Average Precision) using YOLOv5, and recommends personalized music playlists. YOLOv5 and Spotify API are utilized to create a seamless and intuitive user experience.