Air violin: a machine learning approach to fingering gesture recognition

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dc.contributor.author	Dalmazzo, David
dc.contributor.author	Ramírez, Rafael,1966-
dc.date	2017
dc.identifier.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
dc.identifier.citation	978-1-4503-5557-5
dc.identifier.citation	http://dx.doi.org/10.1145/3139513.3139526
dc.identifier.uri	http://hdl.handle.net/10230/41697
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	ACM Association for Computer Machinery
dc.relation	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.
dc.relation	info:eu-repo/grantAgreement/EC/H2020/688269
dc.relation	info:eu-repo/grantAgreement/ES/1PE/TIN 2013-48152-C2-2-R
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Association for Computer Machinery, http://doi.acm.org/10.1145/3139513.3139526
dc.subject	Gestures
dc.subject	Machine Learning
dc.subject	Hand tracking
dc.subject	HMM
dc.subject	Gamification
dc.subject	Violin
dc.subject	Music education
dc.title	Air violin: a machine learning approach to fingering gesture recognition
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/acceptedVersion
dc.description.abstract	Comunicació presentada a: 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education, celebrat el 13 de novembre de 2017 a Glasgow, Regne Unit.
dc.description.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.
dc.description.abstract	This work has been partly sponsored by the Spanish TIN project TIMUL (TIN 2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).

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