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dc.contributor.authorWang, C
dc.contributor.authorLostanlen, V
dc.contributor.authorBenetos, E
dc.contributor.authorChew, E
dc.contributor.authorIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)
dc.date.accessioned2020-04-15T14:50:56Z
dc.date.available2020-01-24
dc.date.available2020-04-15T14:50:56Z
dc.date.issued2020-05-04
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/63588
dc.description.abstractPlaying techniques are important expressive elements in music signals. In this paper, we propose a recognition system based on the joint time–frequency scattering transform (jTFST) for pitch evolution-based playing techniques (PETs), a group of playing techniques with monotonic pitch changes over time. The jTFST represents spectro-temporal patterns in the time–frequency domain, capturing discriminative information of PETs. As a case study, we analyse three commonly used PETs of the Chinese bamboo flute: acciacatura, portamento, and glissando, and encode their characteristics using the jTFST. To verify the proposed approach, we create a new dataset, the CBF-petsDB, containing PETs played in isolation as well as in the context of whole pieces performed and annotated by professional players. Feeding the jTFST to a machine learning classifier, we obtain F-measures of 71% for acciacatura, 59% for portamento, and 83% for glissando detection, and provide explanatory visualisations of scattering coefficients for each technique.en_US
dc.format.extent? - ? (5)
dc.publisherIEEEen_US
dc.subjectMusic signal analysisen_US
dc.subjectScattering transformen_US
dc.subjectPerformance analysisen_US
dc.subjectPlaying technique recognitionen_US
dc.titlePlaying Technique Recognition by Joint Time–Frequency Scatteringen_US
dc.typeConference Proceedingen_US
dc.rights.holder© 2020 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.
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2020-01-24
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderA Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineeringen_US
rioxxterms.funder.project483cf8e1-88a1-4b8b-aecb-8402672d45f8en_US


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