SMERS: Music emotion recognition using support vector regression

Music emotion plays an important role in music retrieval, mood detection and other music-related applications. Many issues for music emotion recognition have been addressed by different disciplines such as physiology, psychology, cognitive science and musicology. We present a support vector regression (SVR) based music emotion recognition system. The recognition process consists of three steps: (i) seven distinct features are extracted from music; (ii) those features are mapped into eleven emotion categories on Thayer's two-dimensional emotion model; (iii) two regression functions are trained using SVR and then arousal and valence values are predicted. We have tested our SVR-based emotion classifier in both Cartesian and polar coordinate system empirically. The result indicates the SVR classifier in the polar representation produces satisfactory result which reaches 94.55% accuracy superior to the SVR (in Cartesian) and other machine learning classification algorithms such as SVM and GMM.