01156nas a2200157 4500008004100000022001800041245007100059210006900130260002800199300001400227520066000241100002000901700002400921700001700945856003600962 2016 eng d a978145033362700aMachine Learning of Personal Gesture Variation in Music Conducting0 aMachine Learning of Personal Gesture Variation in Music Conducti aSan Jose, CAbACM Press a3428-34323 a
This note presents a system that learns expressive and idiosyncratic gesture variations for gesture-based interaction. The system is used as an interaction technique in a music conducting scenario where gesture variations drive music articulation. A simple model based on Gaussian Mixture Modeling is used to allow the user to configure the system by providing variation examples. The system performance and the influence of user musical expertise is evaluated in a user study, which shows that the model is able to learn idiosyncratic variations that allow users to control articulation, with better performance for users with musical expertise.
1 aSarasua, Alvaro1 aCaramiaux, Baptiste1 aTanaka, Atau uhttp://phenicx.upf.edu/node/277