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dc.contributor.authorSANDLER, MBen_US
dc.contributor.authorquinton, Een_US
dc.contributor.authoro'hanlon, Ken_US
dc.contributor.authordixon, Sen_US
dc.contributor.authorDMRN+11: Digital Music Research Network One-day Workshop 2016en_US
dc.date.accessioned2017-02-10T16:20:14Z
dc.date.available2016-12-20en_US
dc.date.issued2016-12-20en_US
dc.date.submitted2017-02-10T11:58:37.230Z
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/19348
dc.description.abstractMeter inference algorithms are typically designed to track metrical structure in presence of mild deviations of the feature estimates over time in order to account for performance imprecisions, expressive timing or musical effects such as accelerando. Abrupt changes of metrical structure over time are comparatively rarely addressed. In this paper, we present an unsupervised approach to detect metrical structure changes. Formulating the problem as a metrical structure based segmentation retrieval task, we present a variant of sparse Non-negative Matrix Factorisation (NMF) and demonstrate state of the art performance.en_US
dc.publisherCentre for Digital Music, Queen Mary University of Londonen_US
dc.titleAutomatic Detection of Metrical Structure Changesen_US
dc.typeConference Proceeding
dc.rights.holder© Centre for Digital Music, Queen Mary University of London
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.publisher-urlhttps://qmro.qmul.ac.uk/xmlui/handle/123456789/19345en_US
dcterms.dateAccepted2016-12-20en_US


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