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Air Violin: A Machine Learning Approach to Fingering Gesture Recognition

Title Air Violin: A Machine Learning Approach to Fingering Gesture Recognition
Publication Type Conference Paper
Year of Publication 2017
Conference Name MIE’17, November 13, 2017, Glasgow, UK
Authors Dalmazzo, D. C. , & Ramirez R.
Pagination 4
Conference Start Date 13/11/2017
Publisher Proceedings of 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education (MIE’17). ACM, New York, NY, USA
Conference Location Glasgow - Scotland
Abstract We train and evaluate two machine learning models for predicting fingering in violin performances using motion and EMG sensors integrated in the Myo device. Our aim is twofold: first, provide a fingering recognition model in the context of a gamification virtual violin application where we measure both right hand (i.e. bow) and left hand (i.e. ngering) gestures, and second, implement a tracking system for a computer assisted pedagogical tool for self-regulated learners in high-level music education. Our approach is based on the principle of mapping-by-demonstration in which the model is trained by the performer. We evaluated a model based on Decision Trees and compared it with a Hidden Markovian Model.
preprint/postprint document http://hdl.handle.net/10230/41697
Final publication http://dx.doi.org/10.1145/3139513.3139526