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Machine Learning of Personal Gesture Variation in Music Conducting

Sarasua, Alvaro; Caramiaux, Baptiste and Tanaka, Atau. 2016. Machine Learning of Personal Gesture Variation in Music Conducting. CHI '16: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, [Article]

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Abstract or Description

This note presents a system that learns expressive and id- iosyncratic gesture variations for gesture-based interaction. The system is used as an interaction technique in a music con- ducting scenario where gesture variations drive music articu- lation. A simple model based on Gaussian Mixture Modeling is used to allow the user to configure the system by provid- ing variation examples. The system performance and the in- fluence of user musical expertise is evaluated in a user study, which shows that the model is able to learn idiosyncratic vari- ations that allow users to control articulation, with better per- formance for users with musical expertise.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1145/2858036.2858328

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
May 2016Published
8 January 2016Accepted

Item ID:

17510

Date Deposited:

23 Mar 2016 08:46

Last Modified:

09 Jul 2018 13:05

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/17510

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