A Music Recommendation System with Consideration of Personal Emotion

Abstract

Emotions evoked by were analyzed. An estimation of the correlation coefficient was applied to determine features of music that evoke an emotion. These features were then used to train two support vector machines (SVMs) for an individual subject to classify music that evokes happiness, anger, sadness, and peacefulness. The proposed approach can be used to classify music that evokes an emotion and to build a personal emotion-cognitive music recommendation system for an individual subject. Experiment results show the effectiveness of the proposed approach.