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dc.contributor.authorSuchánek, Tomáš
dc.date.accessioned2021-07-21T07:06:58Z
dc.date.available2021-07-21T07:06:58Z
dc.date.issued2021cs
dc.identifier.citationProceedings I of the 27st Conference STUDENT EEICT 2021: General papers. s. 291-294. ISBN 978-80-214-5942-7cs
dc.identifier.isbn978-80-214-5942-7
dc.identifier.urihttp://hdl.handle.net/11012/200765
dc.description.abstractThis thesis deals with systems for tempo and beat detection in music recordings, whosefunctionality is based on neural networks. The basic structure of such systems is briefly described andthe emphasis is then placed on a comparison of recurrent and temporal convolutional networks, whichhave proven to be the most suitable for this task. The main outcome of this work is then proposaland comparison of modified temporal convolutional network with other state-of-the-art networks ina beat tracking system. The results suggest that simplification in existing architectures could benefitfrom faster training times, while it maintains or slightly improves the accuracy of a detection system.en
dc.formattextcs
dc.format.extent291-294cs
dc.format.mimetypeapplication/pdfen
dc.language.isocscs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 27st Conference STUDENT EEICT 2021: General papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectBeat trackingen
dc.subjectmachine learningen
dc.subjectneural networken
dc.subjectsignal processingen
dc.titleBeat Tracking System Based On A Neural Networken
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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