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