Abstract
Links between music and body motion can be studied through experiments called sound-tracing. One of the main challenges in such research is to develop robust analysis techniques that are able to deal with the multidimensional data that musical sound and body motion present. The article evaluates four different analysis methods applied to an experiment in which participants moved their hands following perceptual features of short sound objects. Motion capture data has been analyzed and correlated with a set of quantitative sound features using four different methods: (a) a pattern recognition classifier, (b) t-tests, (c) Spearman s Á correlation, and (d) canonical correlation. This article shows how the analysis methods complement each other, and that applying several analysis techniques to the same data set can broaden the knowledge gained from the experiment.
Copyright ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The de nitive version was published in ACM Transactions on Applied Perception 10(2).