Abstract
Music is often discussed to be emotional because it reflects expressive movements in audible form. Thus, a valid approach to measure musical emotion could be to assess movement stimulated by music. In two experiments we evaluated the discriminative power of mobile-device generated acceleration data produced by free movement during music listening for the prediction of ratings on the Geneva Emotion Music Scales (GEMS-9). The quality of prediction for different dimensions of GEMS varied between experiments for tenderness (R12(first experiment) = 0.50, R22(second experiment) = 0.39), nostalgia (R12 = 0.42, R22 = 0.30), wonder (R12 = 0.25, R22 = 0.34), sadness (R12 = 0.24, R22 = 0.35), peacefulness (R12 = 0.20, R22 = 0.35) and joy (R12 = 0.19, R22 = 0.33) and transcendence (R12 = 0.14, R22 = 0.00). For others like power (R12 = 0.42, R22 = 0.49) and tension (R12 = 0.28, R22 = 0.27) results could be almost reproduced. Furthermore, we extracted two principle components from GEMS ratings, one representing arousal and the other one valence of the experienced feeling. Both qualities, arousal and valence, could be predicted by acceleration data, indicating, that they provide information on the quantity and quality of experience. On the one hand, these findings show how music-evoked movement patterns relate to music-evoked feelings. On the other hand, they contribute to integrate findings from the field of embodied music cognition into music recommender systems.
Highlights
Music is often used to regulate emotions like reducing stress or to influence one’s mood as shown by [1] or [2]
In two experiments we evaluated the discriminative power of mobile-device generated acceleration data produced by free movement during music listening for the prediction of ratings on the Geneva Emotion Music Scales (GEMS-9)
Sadness, joy, peacefulness and wonder scored much higher in terms of R2 whereas nostalgia, tension and transcendence were among the GEMS being most difficult to predict
Summary
Music is often used to regulate emotions like reducing stress or to influence one’s mood as shown by [1] or [2]. This means that listening to music is highly linked to the experience of emotions [3,4,5]. These subjective qualities of music do still play a minor role in the field of Music Information Retrieval (MIR) and Music Recommender Systems (MRS), i.e. for the retrieval and recommendation of music offered by web-based services [6]. Considering acoustical features of music and its implications on the perception of emotions does not yet consider the motor origin hypothesis of emotion in music as claimed
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