Computational modelling of expressive music performance in hexaphonic guitar

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• dc.contributor.author Siquier, Marc
• dc.contributor.author Giraldo, Sergio
• dc.contributor.author Ramírez, Rafael,1966-
• dc.date.accessioned 2019-12-19T09:50:16Z
• dc.date.available 2019-12-19T09:50:16Z
• 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 Computational modelling of expressive music performance has been widely studied in the past. While previous work in this area has been mainly focused on classical piano music, there has been very little work on guitar music, and such work has focused on monophonic guitar playing. In this work, we present a machine learning approach to automatically generate expressive performances from non expressive music scores for polyphonic guitar. We treated guitar as an hexaphonic instrument, obtaining a polyphonic transcription of performed musical pieces. Features were extracted from the scores and performance actions were calculated from the deviations of the score and the performance. Machine learning techniques were used to train computational models to predict the aforementioned performance actions. Qualitative and quantitative evaluations of the models and the predicted pieces were performed.
• 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 Siquier M, Giraldo S, Ramirez R. Computational modelling of expressive music performance in hexaphonic guitar. In: Ramírez R, Conklin D, Iñesta JM, editors. Proceedings of the 10th International Workshop on Machine Learning and Music (MML 2017); 2017 Oct 6; Barcelona, Spain. Barcelona: MML; 2017. p. 61-6.
• dc.identifier.uri http://hdl.handle.net/10230/43211
• 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 Computational models
• dc.subject.keyword Expressive music performance
• dc.subject.keyword Hexaphonic guitar
• dc.title Computational modelling of expressive music performance in hexaphonic guitar
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion