Abstract
The high dimensionality of music evoked movement data makes it difficult to uncover the fundamental aspects of human music-movement associations. However, modeling these data via Dirichlet process mixture (DPM) Models facilitates this task considerably. In this paper we present DPM models to investigate positional and directional aspects of music evoked bodily movement. In an experimental study subjects were moving spontaneously on a musical piece that was characterized by passages of extreme contrasts in physical acoustic energy. The contrasts in acoustic energy caused surprise and triggered new gestural behavior. We used sparsity as a key indicator for surprise and made it visible in two ways. Firstly as the result of a positional analysis using a Dirichlet process gaussian mixture model (DPGMM) and secondly as the result of a directional analysis using a Dirichlet process multinomial mixture model (DPMMM). The results show that gestural response follows the surprising or unpredictable character of the music.
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Amelynck, D., Maes, PJ., Leman, M., Martens, JP. (2016). The Surprising Character of Music: A Search for Sparsity in Music Evoked Body Movements. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_36
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DOI: https://doi.org/10.1007/978-3-319-25226-1_36
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