Music Emotion Recognition using Gaussian Processes

This paper describes the music emotion recognition system developed at the University of Aizu for the Emotion in Music task of the MediaEval’2013 benchmark evaluation campaign. A set of standard feature types provided by the Marsyas toolkit was used to parametrize each music clip. Arousal and valence are modeled separately using Gaussian Process regression (GPR). We compared performances of the GPR and Support Vector regression (SVR) and found out that GPR gives better results than SVR for the static per song emotion estimation task. For the dynamic emotion estimation task GPR had some scalability problems and fair comparison was not possible.