Show simple item record

dc.contributor.author Bantula, Helena
dc.contributor.author Giraldo, Sergio
dc.contributor.author Ramírez, Rafael,1966-
dc.date.accessioned 2017-10-30T09:03:10Z
dc.date.available 2017-10-30T09:03:10Z
dc.date.issued 2016
dc.identifier.citation Bantula H, Giraldo S, Ramirez R. Jazz ensemble expressive performance modeling. In: Devaney J, Mandel MI, Turnbull D, Tzanetakis G, editors. ISMIR 2016. Proceedings of the 17th International Society for Music Information Retrieval Conference; 2016 Aug 7-11; New York City (NY). [Canada]: ISMIR; 2016. p. 674-80.
dc.identifier.uri http://hdl.handle.net/10230/33114
dc.description Comunicació presentada a la 17th International Society for Music Information Retrieval Conference (ISMIR 2016), celebrada els dies 7 a 11 d'agost de 2016 a Nova York, EUA.
dc.description.abstract Computational expressive music performance studies the analysis and characterisation of the deviations that a musician introduces when performing a musical piece. It has been studied in a classical context where timing and dynamic deviations are modeled using machine learning techniques. In jazz music, work has been done previously on the study of ornament prediction in guitar performance, as well as in saxophone expressive modeling. However, little work has been done on expressive ensemble performance. In this work, we analysed the musical expressivity of jazz guitar and piano from two different perspectives: solo and ensemble performance. The aim of this paper is to study the influence of piano accompaniment into the performance of a guitar melody and vice versa. Based on a set of recordings made by professional musicians, we extracted descriptors from the score, we transcribed the guitar and the piano performances and calculated performance actions for both instruments. We applied machine learning techniques to train models for each performance action, taking into account both solo and ensemble descriptors. Finally, we compared the accuracy of the induced models. The accuracy of most models increased when ensemble information was considered, which can be explained by the interaction between musicians.
dc.description.sponsorship This work has been partly sponsored by the Spanish TIN project TIMUL (TIN2013-48152-C2-2-R) and the H2020-ICT-688269 TELMI project.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher International Society for Music Information Retrieval (ISMIR)
dc.relation.ispartof Devaney J, Mandel MI, Turnbull D, Tzanetakis G, editors. ISMIR 2016. Proceedings of the 17th International Society for Music Information Retrieval Conference; 2016 Aug 7-11; New York City (NY). [Canada]: ISMIR; 2016. p. 674-80.
dc.rights Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: . “Jazz Ensemble Expressive Performance Modeling”, 17th International Society for Music Information Retrieval Conference, 2016.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Música -- Anàlisi
dc.title Jazz ensemble expressive performance modeling
dc.type info:eu-repo/semantics/conferenceObject
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.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

Compliant to Partaking