A Real-time feedback learning tool to visualize sound quality in violin performances

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• dc.contributor.author Giraldo, Sergio
• dc.contributor.author Ramírez, Rafael,1966-
• dc.contributor.author Waddell, George
• dc.contributor.author Williamon, Aaron
• dc.date.accessioned 2019-12-19T09:35:53Z
• dc.date.available 2019-12-19T09:35:53Z
• dc.date.issued 2017
• dc.description Comunicació presentada a: 10th International Workshop on Machine Learning and Music (MML), celebrat a Barcelona (Espanya), el 6 d'octubre de 2017.
• dc.description.abstract The assessment of the sound properties of a performed mu- sical note has been widely studied in the past. Although a consensus exist on what is a good or a bad musical performance, there is not a formal de nition of performance tone quality due to its subjectivity. In this study we present a computational approach for the automatic assess- ment of violin sound production. We investigate the correlations among extracted features from audio performances and the perceptual quality of violin sounds rated by listeners using machine learning techniques. The obtained models are used for implementing a real-time feedback learning system.
• 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 inno- vation 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 S, Ramirez R, Waddell G, Williamon A. A Real-time feedback learning tool to visualize sound quality in violin performances. In: Ramirez R, Conklin D, Iñesta JM, editors. 10th International Workshop on Machine Learning and Music; 2017 Oct 6; Barcelona, Spain. Barcelona: MML; 2017. p. 19-24.
• dc.identifier.uri http://hdl.handle.net/10230/43210
• dc.language.iso eng
• dc.publisher Machine Learning and Music (MML)
• dc.relation.ispartof Ramirez R, Conklin D, Iñesta JM, editors. 10th International Workshop on Machine Learning and Music; 2017 Oct 6; Barcelona, Spain. Barcelona: MML; 2017.
• 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 The Authors. CC BY-NC 4.0. Reconocimiento-No comercial 4.0 Internacional
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by-nc/4.0/
• dc.subject.keyword Machine learning
• dc.subject.keyword Violin sound quality
• dc.subject.keyword Automatic assessment
• dc.subject.keyword Timbre dimensions
• dc.subject.keyword Audio features
• dc.title A Real-time feedback learning tool to visualize sound quality in violin performances
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion