Air violin: a machine learning approach to fingering gesture recognition

Dalmazzo, David; Ramírez, Rafael,1966-

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dc.contributor.author	Dalmazzo, David
dc.contributor.author	Ramírez, Rafael,1966-
dc.date.accessioned	2019-06-04T08:38:36Z
dc.date.available	2019-06-04T08:38:36Z
dc.date.issued	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.isbn	978-1-4503-5557-5
dc.identifier.uri	http://hdl.handle.net/10230/41697
dc.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.
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.sponsorship	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).
dc.format.mimetype	application/pdf
dc.language.iso	eng
dc.publisher	ACM Association for Computer Machinery
dc.relation.ispartof	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.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.title	Air violin: a machine learning approach to fingering gesture recognition
dc.type	info:eu-repo/semantics/article
dc.identifier.doi	http://dx.doi.org/10.1145/3139513.3139526
dc.subject.keyword	Gestures
dc.subject.keyword	Machine Learning
dc.subject.keyword	Hand tracking
dc.subject.keyword	HMM
dc.subject.keyword	Gamification
dc.subject.keyword	Violin
dc.subject.keyword	Music education
dc.relation.projectID	info:eu-repo/grantAgreement/EC/H2020/688269
dc.relation.projectID	info:eu-repo/grantAgreement/ES/1PE/TIN 2013-48152-C2-2-R
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.type.version	info:eu-repo/semantics/acceptedVersion