Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95810
DC FieldValueLanguage
dc.contributor.authorPinto, António-
dc.contributor.authorBöck, Sebastian-
dc.contributor.authorCardoso, Jaime-
dc.contributor.authorDavies, Matthew-
dc.date.accessioned2021-09-24T16:13:53Z-
dc.date.available2021-09-24T16:13:53Z-
dc.date.issued2021-
dc.identifier.issn2079-9292pt
dc.identifier.urihttp://hdl.handle.net/10316/95810-
dc.description.abstractThe extraction of the beat from musical audio signals represents a foundational task in the field of music information retrieval. While great advances in performance have been achieved due the use of deep neural networks, significant shortcomings still remain. In particular, performance is generally much lower on musical content that differs from that which is contained in existing annotated datasets used for neural network training, as well as in the presence of challenging musical conditions such as rubato. In this paper, we positioned our approach to beat tracking from a real-world perspective where an end-user targets very high accuracy on specific music pieces and for which the current state of the art is not effective. To this end, we explored the use of targeted fine-tuning of a state-of-the-art deep neural network based on a very limited temporal region of annotated beat locations. We demonstrated the success of our approach via improved performance across existing annotated datasets and a new annotation-correction approach for evaluation. Furthermore, we highlighted the ability of content-specific fine-tuning to learn both what is and what is not the beat in challenging musical conditions.pt
dc.language.isoengpt
dc.publisherMDPIpt
dc.relationIF/01566/2015pt
dc.relationSFRH/BD/120383/2016pt
dc.relationCISUC/UID/CEC/00326/2020pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectBeat trackingpt
dc.subjectTransfer learningpt
dc.subjectUser adaptationpt
dc.titleUser-Driven Fine-Tuning for Beat Trackingpt
dc.typearticle-
degois.publication.firstPage1518pt
degois.publication.issue13pt
degois.publication.titleElectronics (Switzerland)pt
dc.peerreviewedyespt
dc.identifier.doi10.3390/electronics10131518pt
degois.publication.volume10pt
dc.date.embargo2021-01-01*
uc.date.periodoEmbargo0pt
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltextCom Texto completo-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
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