Emotion Based Music Recommendation System

The integration of emotion state and music recommendation systems holds a high potential for enhancing experiences through mapping emotion state with preference for music. In this work, an emotion state-dependent music recommendation system is proposed, utilizing face expression recognition for determination of emotion state and real-time suggesting of music in relation to it. With Convolutional Neural Networks (CNNs) and the FER-2013 dataset, emotion categories including happiness, sadness, anger, surprise, and neutrality are determined. A dynamic module then retrieves personalized playlists of music in relation to detected emotion, offering real-time, adaptable recommendations to users. Computer vision techniques such as Haar Cascade classifiers are utilized for face feature extraction and emotion classification through machine learning algorithms. Outcomes are represented through confusion matrices, classification reports, and emotion distribution plots, providing an insight into performance of the system. The proposed model introduces a new direction for personalized consumption of music, enhancing emotion well-being through music and opening doors for future emotion AI breakthroughs, with implications in mental wellness and entertainment.