Air violin: a machine learning approach to fingering gesture recognition

Dalmazzo, David; Ramírez, Rafael, 1966-

doi:http://dx.doi.org/10.1145/3139513.3139526

Air violin: a machine learning approach to fingering gesture recognition

Citation

Dalmazzo D, Ramirez R. Air violin: a machine learning approach to fingering gesture recognition. In: Volpe G, Gori M, Bianchi-Berthouze N, Baud-Bovy G, Alborno P, Volta E, editors. 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education; 2017 Nov 13; Glasgow, United Kingdom. New York: Association for Computer Machinery; 2017. p. 63-6. DOI: 10.1145/3139513.3139526

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. fingering) 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.

Description

Comunicació presentada a: 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education, celebrat el 13 de novembre de 2017 a Glasgow, Regne Unit.