Automatic assessment of tone quality in violin music performance
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- dc.contributor.author Giraldo, Sergio
- dc.contributor.author Waddell, George
- dc.contributor.author Nou Plana, Ignasi
- dc.contributor.author Ortega, Ariadna
- dc.contributor.author Mayor, Oscar
- dc.contributor.author Perez, Alfonso
- dc.contributor.author Williamon, Aaron
- dc.contributor.author Ramírez, Rafael,1966-
- dc.date.accessioned 2019-12-23T11:35:26Z
- dc.date.available 2019-12-23T11:35:26Z
- dc.date.issued 2019
- dc.description.abstract The automatic assessment of music performance has become an area of increasing interest due to the growing number of technology-enhanced music learning systems. In most of these systems, the assessment of musical performance is based on pitch and onset accuracy, but very few pay attention to other important aspects of performance, such as sound quality or timbre. This is particularly true in violin education, where the quality of timbre plays a significant role in the assessment of musical performances. However, obtaining quantifiable criteria for the assessment of timbre quality is challenging, as it relies on consensus among the subjective interpretations of experts. We present an approach to assess the quality of timbre in violin performances using machine learning techniques. We collected audio recordings of several tone qualities and performed perceptual tests to find correlations among different timbre dimensions. We processed the audio recordings to extract acoustic features for training tone-quality models. Correlations among the extracted features were analyzed and feature information for discriminating different timbre qualities were investigated. A real-time feedback system designed for pedagogical use was implemented in which users can train their own timbre models to assess and receive feedback on their performances.
- dc.description.sponsorship This work has been partly sponsored by the Spanish TIN project TIMUL (TIN2013-48152-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 Giraldo SI, Ramírez R, Mayor O, Nou I, Ortega A, Perez-Carrillo A, Waddell G, Williamon A. Automatic assessment of tone quality in violin music performance. Front Psychol. 2019;10:334. DOI: 10.3389/fpsyg.2019.00334
- dc.identifier.doi http://dx.doi.org/10.3389/fpsyg.2019.00334
- dc.identifier.issn 1664-1078
- dc.identifier.uri http://hdl.handle.net/10230/43229
- dc.language.iso eng
- dc.publisher Frontiers
- dc.relation.ispartof Front Psychol. 2019;10:334.
- 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 © 2019 Giraldo, Ramirez, Mayor, Nou, Ortega, Perez-Carrillo, Waddell and Williamon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Automatic assessment of music
- dc.subject.keyword Machine learning
- dc.subject.keyword Violin performance
- dc.subject.keyword Tone quality
- dc.subject.keyword Music performance
- dc.title Automatic assessment of tone quality in violin music performance
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion