TY - Generic T1 - Machine Learning of Personal Gesture Variation in Music Conducting T2 - CHI - Human Factors in Computing Systems Y1 - 2016 A1 - Sarasua, Alvaro A1 - Caramiaux, Baptiste A1 - Tanaka, Atau AB -
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.JF - CHI - Human Factors in Computing Systems PB - ACM Press CY - San Jose, CA ER - TY - CONF T1 - Beat Tracking from Conducting Gestural Data: A Multi-Subject Study T2 - Proceedings of the 2014 International Workshop on Movement and Computing Y1 - 2014 A1 - Sarasua, Alvaro A1 - Guaus, Enric KW - beat tracking KW - classical music KW - conducting KW - expressive performance KW - motion capture AB -
Body movement has received increasing attention in music technology research during the last years. Some new mu- sical interfaces make use of gestures to control music in a meaningful and intuitive way. A typical approach is to use the orchestra conducting paradigm, in which the computer that generates the music would be a virtual orchestra con- ducted by the user. However, although conductors’ gestures are complex and their meaning can vary depending on the musical context, this context-dependency is still to explore. We propose a method to study context-dependency of body and facial gestures of conductors in orchestral classical mu- sic based on temporal clustering of gestures into actions, followed by an analysis of the evolution of audio features after action occurrences. For this, multi-modal data (audio, video, motion capture) will be recorded in real live concerts and rehearsals situations using unobtrusive techniques.
JF - ACM Multimedia CY - Barcelona ER -