Bowing modeling for violin students assistance
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- dc.contributor.author Ortega, Fabio J. M.
- dc.contributor.author Giraldo, Sergio
- dc.contributor.author Ramírez, Rafael,1966-
- dc.date.accessioned 2019-04-16T07:17:45Z
- dc.date.available 2019-04-16T07:17:45Z
- dc.date.issued 2017
- dc.description Comunicació presentada a: 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education, que va tenir lloc el 13 de novembre de 2017 a Glasgow, UK.
- dc.description.abstract Though musicians tend to agree on the importance of practicing expressivity in performance, not many tools and techniques are available for the task. A machine learning model is proposed for predicting bowing velocity during performances of violin pieces. Our aim is to provide feedback to violin students in a technology–enhanced learning setting. Predictions are generated for musical phrases in a score by matching them to melodically and rhythmically similar phrases in performances by experts and adapting the bow velocity curve measured in the experts’ performance. Results show that mean error in velocity predictions and bowing direction classification accuracy outperform our baseline when reference phrases similar to the predicted ones are available.
- 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 Ortega FJM, Giraldo SI, Ramírez R. Bowing modeling for violin students assistance. In: Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education; 2017 Nov 13-17; Glasgow, Scotland. New York: ACM; 2017. p. 60-2. DOI: 10.1145/3139513.3139525
- dc.identifier.doi http://dx.doi.org/10.1145/3139513.3139525
- dc.identifier.uri http://hdl.handle.net/10230/37113
- dc.language.iso eng
- dc.publisher ACM Association for Computer Machinery
- dc.relation.ispartof Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education; 2017 Nov 13-17; Glasgow, Scotland. New York: ACM; 2017. p. 60-2.
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688269
- dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2013-48152-C2-2-R
- dc.rights © 2017 Association for Computing Machinery
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword Expressive performance modeling
- dc.subject.keyword Machine learning
- dc.subject.keyword Violin
- dc.subject.keyword Music
- dc.subject.keyword Education
- dc.title Bowing modeling for violin students assistance
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/acceptedVersion