Computational Modeling of Induced Emotion Using GEMS

Most researchers in the automatic music emotion recognition field focus on the two-dimensional valence and arousal model. This model though does not account for the whole diversity of emotions expressible through music. Moreover, in many cases it might be important to model induced (felt) emotion, rather than perceived emotion. In this paper we explore a multidimensional emotional space, the Geneva Emotional Music Scales (GEMS), which addresses these two issues. We collected the data for our study using a game with a purpose. We exploit a comprehensive set of features from several state-of-the-art toolboxes and propose a new set of harmonically motivated features. The performance of these feature sets is compared. Additionally, we use expert human annotations to explore the dependency between musicologically meaningful characteristics of music and emotional categories of GEMS, demonstrating the need for algorithms that can better approximate human perception.