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.accessioned 2019-06-04T08:38:36Z
• dc.date.available 2019-06-04T08:38:36Z
• dc.date.issued 2017
• 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.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.doi http://dx.doi.org/10.1145/3139513.3139526
• dc.identifier.isbn 978-1-4503-5557-5
• dc.identifier.uri http://hdl.handle.net/10230/41697
• 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.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 © 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.rights.accessRights info:eu-repo/semantics/openAccess
• 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.title Air violin: a machine learning approach to fingering gesture recognition
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion