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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.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


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