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Automatic assessment of violin performance using dynamic time warping classification

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dc.contributor.author Giraldo, Sergio
dc.contributor.author Ortega, Ariadna
dc.contributor.author Pérez Carrillo, Alfonso Antonio, 1977-
dc.contributor.author Ramírez, Rafael,1966-
dc.contributor.author Waddell, George
dc.contributor.author Williamon, Aaron
dc.date.accessioned 2019-03-14T09:51:13Z
dc.date.available 2019-03-14T09:51:13Z
dc.date.issued 2018
dc.identifier.citation Giraldo S, Ortega A, Perez A, Ramirez R, Waddell G, Williamon A. Automatic assessment of violin performance using dynamic time warping classification. In: 26th Signal Processing and Communications Applications Conference; 2018 May 2-5; Izmir, Turkey. Nova Jersey: Institute of Electrical and Electronics Engineers; 2018. DOI: 10.1109/SIU.2018.8404556
dc.identifier.uri http://hdl.handle.net/10230/36825
dc.description Comunicació presentada a: 26th Signal Processing and Communications Applications Conference, celebrada a Izmir, Turkey, del 2 al 5 de maig de 2018.
dc.description.abstract The automatic assessment of music performance has become an area of special interest due to the increasing amount of technology-enhanced music learning systems. However, in most of these systems the assessment of the musical performance is based on the accuracy of onsets and pitch, paying little attention to other relevant aspects of performance. In this paper we present a preliminary study to assess the quality of violin performance using machine learning techniques. We collect recording examples of selected violin exercises varying from expert to amateur performances. We process the audio signal to extract features to train models using clustering based on Dynamic Time Warping distance. The quality of new performances is evaluated based on the level of match/miss-match to each of the recorded training examples.
dc.description.sponsorship This work has been partly sponsored by the Spanish TIN project TIMUL (TIN 2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation 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.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof 26th Signal Processing and Communications Applications Conference; 2018 May 2-5; Izmir, Turkey. Nova Jersey: Institute of Electrical and Electronics Engineers; 2018.
dc.rights © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at https://dx.doi.org/10.1109/SIU.2018.8404556
dc.title Automatic assessment of violin performance using dynamic time warping classification
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1109/SIU.2018.8404556
dc.subject.keyword Music
dc.subject.keyword Feature extraction
dc.subject.keyword Machine learning
dc.subject.keyword Training
dc.subject.keyword Music information retrieval
dc.subject.keyword Time series analysis
dc.subject.keyword Hidden Markov models
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.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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