Embodied Meter Revisited

Abstract

Previous research has shown that humans tend to embody musical meter at multiple beat levels during spontaneous dance. This work that been based on identifying typical periodic movement patterns, or eigenmovements, and has relied on time-domain analyses. The current study: 1) presents a novel method of using time-frequency analysis in conjunction with group-level tensor decomposition; 2) compares its results to time-domain analysis, and 3) investigates how the amplitude of eigenmovements depends on musical content and genre. Data comprised three-dimensional motion capture of 72 participants’ spontaneous dance movements to 16 stimuli including eight different genres. Each trial was subjected to a discrete wavelet transform, concatenated into a trial-space-frequency tensor and decomposed using tensor decomposition. Twelve movement primitives, or eigenmovements, were identified, eleven of which were frequency locked with one of four metrical levels. The results suggest that time-frequency decomposition can more efficiently group movement directions together. Furthermore, the employed group-level decomposition allows for a straightforward analysis of interstimulus and interparticipant differences in music-induced movement. Amplitude of eigenmovements was found to depend on the amount of fluctuation in the music in particularly at one- and two-beat levels.