Dynamic Music Emotion Recognition Using State-Space Models

This paper describes the temporal music emotion recognition system developed at the University of Aizu for the Emotion in Music task of the MediaEval 2014 benchmark evaluation campaign. The arousal-valence trajectory prediction is cast as a time series filtering task and is modeled by a statespace models. These models include standard linear model (Kalman filter) as well as novel non-linear, non-parametric Gaussian Processes based dynamic system. The music signal was parametrized using standard features extracted with the Marsyas toolkit. Based on the preliminary results obtained from small random validation set, clear advantage of any feature or model could not be observed.