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dc.contributor.authorZhao, Y
dc.contributor.authorFazekas, G
dc.contributor.authorSandler, M
dc.date.accessioned2021-10-21T09:06:13Z
dc.date.available2021-10-21T09:06:13Z
dc.date.issued2020-01-01
dc.identifier.isbn9788894541502
dc.identifier.issn2518-3672
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/74639
dc.description.abstractThe same piece of music can be performed in various styles by different performers. Vibrato plays an important role in violin players' emotional expression, and it is an important factor of playing style while execution shows great diversity. Expressive timing is also an important factor to reflect individual play styles. In our study, we construct a novel dataset, which contains 15 concertos performed by 9 master violinists. Four vibrato features and one timing feature are extracted from the data, and we present a method based on the similarity of feature distribution to identify violinists using each feature alone and fusion of features. The result shows that vibrato features are helpful for the identification, but the timing feature performs better, yielding a precision of 0.751. In addition, although the accuracy obtained from fused features are lower than using timing alone, discrimination for each performer is improved.en_US
dc.format.extent185 - 192
dc.publisherSound and Music Computing Networken_US
dc.rightsThis item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.titleIdentifying master violinists using note-level audio featuresen_US
dc.typeConference Proceedingen_US
dc.rights.holder© 2020, The Author(s)
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume2020-Juneen_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Except where otherwise noted, this item's license is described as This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.