Air violin: a machine learning approach to fingering gesture recognition

David Dalmazzo, Rafael Ramirez
2017 Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education - MIE 2017  
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. fingering) gestures, and second, implement a tracking system for a computer assisted pedagogical tool for self-regulated learners in high-level music
more » ... igh-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.
doi:10.1145/3139513.3139526 dblp:conf/icmi/DalmazzoR17 fatcat:bnbiotaahndinai4iong74hpn4