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| Content Provider | ACM Digital Library |
|---|---|
| Author | Godøy, Rolf Inge Torresen, Jim Nymoen, Kristian Jensenius, Alexander Refsum |
| Copyright Year | 2013 |
| Abstract | Links between music and body motion can be studied through experiments called $\textit{sound-tracing}.$ 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) $\textit{t}-tests,$ (c) Spearman's ρ 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. |
| Starting Page | 1 |
| Ending Page | 22 |
| Page Count | 22 |
| File Format | |
| ISSN | 15443558 |
| e-ISSN | 15443965 |
| DOI | 10.1145/2465780.2465783 |
| Volume Number | 10 |
| Issue Number | 2 |
| Journal | ACM Transactions on Applied Perception (TAP) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-06-04 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Sound-tracing Music-related motion |
| Content Type | Text |
| Resource Type | Article |
| Subject | Experimental and Cognitive Psychology Theoretical Computer Science Computer Science |
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