Music is often discussed to be perceived as emotional because it renders expressive movements into audible musical structures. Thus, a valid approach to measure musical emotion could be to assess movement stimulated by music. This study evaluated the discriminative power of mobile-device generated acceleration data produced by free movement during music listening experience for the prediction of different degrees of the Geneva Emotion Music Scales (GEMS-9). The variance of the perceived GEMS-states can be explained by the fitted models as follows: power (r2 = .36), sadness (r2 = .17), tenderness (r2 = .14), joy (r2 = .15), tension (r2 = .11), peacefulness (r2 = .08), nostalgia (r2 = .07), wonder (r2 = .07) and transcendence (r2 = .02). These findings will contribute to understand, how acceleration data can be used to integrate embodied music cognition into Music Recommender Systems.
|Title of host publication||Proceedings of the 11th International Symposium on Computer Music Multidisciplinary Research (CMMR) Music, Mind, and Embodiment|
|Editors||Richard Kronland-Martinet, Mitsuko Aramaki, Sølvi Ystad, Joel Eaton|
|Publisher||Interdisciplinary Centre for Computer Music Research|
|Number of pages||14|
|Publication status||Published - 2015|
- embodiment,emotion,gems,music information retrieval,music recommender systems