A Machine learning approach to discover rules for expressive performance actions in jazz guitar music

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• dc.contributor.author Giraldo, Sergio
• dc.contributor.author Ramírez, Rafael,1966-
• dc.date.accessioned 2019-04-16T08:08:41Z
• dc.date.available 2019-04-16T08:08:41Z
• dc.date.issued 2016
• dc.description.abstract Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.
• dc.description.sponsorship This work has been partly sponsored by the Spanish TIN project TIMUL (TIN2013-48152-C2-2-R), and the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project).
• dc.format.mimetype application/pdf
• dc.identifier.citation Giraldo S, Ramirez R. A Machine learning approach to discover rules for expressive performance actions in jazz guitar music. Front Psychol. 2016 Dec 20;7:1965. DOI: 10.3389/fpsyg.2016.01965
• dc.identifier.doi http://dx.doi.org/10.3389/fpsyg.2016.01965
• dc.identifier.issn 1664-1078
• dc.identifier.uri http://hdl.handle.net/10230/37114
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Psychology. 2016 Dec 20;7:1965
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2013-48152-C2-2-R
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688269
• dc.rights ©2016 Giraldo and Ramirez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor 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 Expressive music performance
• dc.subject.keyword Jazz guitar music
• dc.subject.keyword Ornamentation
• dc.subject.keyword Machine learning
• dc.title A Machine learning approach to discover rules for expressive performance actions in jazz guitar music
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion