The MediaEval 2017 AcousticBrainz genre task: content-based music genre recognition from multiple sources
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- dc.contributor.author Bogdanov, Dmitryca
- dc.contributor.author Porter, Alastairca
- dc.contributor.author Urbano, Juliánca
- dc.contributor.author Schreiber, Hendrikca
- dc.date.accessioned 2017-10-11T17:43:37Z
- dc.date.available 2017-10-11T17:43:37Z
- dc.date.issued 2017
- dc.description Comunicació presentada a: MediaEval2017 Workshop, celebrat del 13 al 15 de setembre de 2017 a Dublin, Irlanda.ca
- dc.description.abstract This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems. We present the task challenges, the employed ground-truth information and datasets, and the evaluation methodology.en
- dc.description.sponsorship This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 (AudioCommons).en
- dc.format.mimetype application/pdf
- dc.identifier.citation Bogdanov D, Porter A, Urbano J, Schreiber H. The MediaEval 2017 AcousticBrainz genre task: content-based music genre recognition from multiple sources. In: Gravier G, Bischke B, Demarty CH, Zaharieva M, Riegler M, Dellandrea E, Bogdanov D, Sutcliffe R, Jones GJF, Larson M. MediaEval2017 Multimedia Benchmark Workshop. Working Notes Proceedings of the MediaEval 2017 Workshop co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017); 2017 Sep 13-15; Dublin, Ireland. Aachen: CEUR; 2017. [3] p.
- dc.identifier.issn 1613-0073
- dc.identifier.uri http://hdl.handle.net/10230/32932
- dc.language.iso eng
- dc.publisher CEUR Workshop Proceedings
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688382
- dc.rights Copyright © 2017 the authors.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword Music genre recognitionen
- dc.subject.keyword Music information retrievalen
- dc.subject.keyword Music datasetsen
- dc.subject.keyword Music genresen
- dc.subject.keyword Music metadataen
- dc.subject.keyword Audio analysisen
- dc.subject.keyword Music classificationen
- dc.subject.keyword Sound and music computingen
- dc.title The MediaEval 2017 AcousticBrainz genre task: content-based music genre recognition from multiple sourcesca
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/publishedVersion