A Machine learning approach to ornamentation modeling and synthesis in jazz guitar

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• dc.contributor.author Ramírez, Rafael,1966-
• dc.contributor.author Giraldo, Sergio
• dc.date.accessioned 2019-05-02T10:25:09Z
• dc.date.available 2019-05-02T10:25:09Z
• dc.date.issued 2016
• dc.description.abstract We present a machine learning approach to automatically generate expressive (ornamented) jazz performances from un-expressive music scores. Features extracted from the scores and the corresponding audio recordings performed by a professional guitarist were used to train computational models for predicting melody ornamentation. As a first step, several machine learning techniques were explored to induce regression models for timing, onset, and dynamics (i.e. note duration and energy) transformations, and an ornamentation model for classifying notes as ornamented or non-ornamented. In a second step, the most suitable ornament for predicted ornamented notes was selected based on note context similarity. Finally, concatenative synthesis was used to automatically synthesize expressive performances of new pieces using the induced models. Supplemental online material for this article containing musical examples of the automatically generated ornamented pieces can be accessed at doi: 10.1080/17459737.2016.1207814 and https://soundcloud.com/machine-learning-and-jazz. In the Online Supplement we present an example of the musical piece Yesterdays by Jerome Kern, which was modeled using our methodology for expressive music performance in jazz guitar.
• dc.description.sponsorship This project has received funding from: the European Union Horizon 2020 research and innovation programme [grant agreement No 688269]; the Spanish TIN project TIMUL [grant agreement TIN2013-48152-C2-2-R].
• dc.format.mimetype application/pdf
• dc.identifier.citation Giraldo S, Ramírez R. A Machine learning approach to ornamentation modeling and synthesis in jazz guitar. Journal of Mathematics and Music: Mathematical and Computational Approaches to Music Theory, Analysis, Composition and Performance. 2016 Oct 17;10(2):107-26. DOI: 10.1080/17459737.2016.1207814
• dc.identifier.doi http://dx.doi.org/10.1080/17459737.2016.1207814
• dc.identifier.issn 1745-9737
• dc.identifier.uri http://hdl.handle.net/10230/37165
• dc.language.iso eng
• dc.publisher Taylor & Francis
• dc.relation.ispartof Journal of Mathematics and Music: Mathematical and Computational Approaches to Music Theory, Analysis, Composition and Performance. 2016 Oct 17;10(2):107-26.
• 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 © This is an Accepted Manuscript of an article published by Taylor & Francis in ournal of Mathematics and Music: Mathematical and Computational Approaches to Music Theory, Analysis, Composition and Performance on 2016 Oct 17, available online: http://www.tandfonline.com/10.1080/17459737.2016.1207814
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Machine learning
• dc.subject.keyword Expressive music performance
• dc.subject.keyword Ornamentation modeling
• dc.subject.keyword Jazz guitar
• dc.subject.keyword Concatenative synthesis
• dc.title A Machine learning approach to ornamentation modeling and synthesis in jazz guitar
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion