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dc.contributor.authorLins, F
dc.contributor.authorJohann, M
dc.contributor.authorBENETOS, E
dc.contributor.authorSchramm, R
dc.contributor.authorIEEE International Conference on Acoustics, Speech, and Signal Processing
dc.date.accessioned2019-03-26T10:24:50Z
dc.date.available2019-02-01
dc.date.available2019-03-26T10:24:50Z
dc.date.issued2019-05-12
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/56489
dc.description.abstractThis paper presents a method for automatic transcription of the diatonic Harmonica instrument. It estimates the multi-pitch activations through a spectrogram factorisation framework. This framework is based on Probabilistic Latent Component Analysis (PLCA) and uses a fixed 4-dimensional dictionary with spectral templates extracted from Harmonica's instrument timbre. Methods based on spectrogram factorisation may suffer from local-optima issues in the presence of harmonic overlap or considerable timbre variability. To alleviate this issue, we propose a set of harmonic constraints that are inherent to the Harmonica instrument note layout or are caused by specific diatonic Harmonica playing techniques. These constraints help to guide the factorisation process until convergence into meaningful multi-pitch activations is achieved. This work also builds a new audio dataset containing solo recordings of diatonic Harmonica excerpts and the respective multi-pitch annotations. We compare our proposed approach against multiple baseline techniques for automatic music transcription on this dataset and report the results based on frame-based F-measure statistics.en_US
dc.format.extent? - ? (5)
dc.publisherIEEEen_US
dc.titleAutomatic Transcription of Diatonic Harmonica Recordingsen_US
dc.typeConference Proceedingen_US
dc.rights.holder© 2019 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.notesNo embargoen_US
pubs.notesIEEE conference, allows uploading postprints at institutional repositories.en_US
pubs.publication-statusAccepteden_US
pubs.publisher-urlhttps://2019.ieeeicassp.org/en_US
dcterms.dateAccepted2019-02-01
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderA Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineeringen_US


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