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The AcousticBrainz genre dataset: Multi-source, multi-level, multi-label, and large-scale

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dc.contributor.author Bogdanov, Dmitry
dc.contributor.author Porter, Alastair
dc.contributor.author Schreiber, Hendrik
dc.contributor.author Urbano, Julián
dc.contributor.author Oramas, Sergio
dc.date.accessioned 2019-07-11T08:27:58Z
dc.date.available 2019-07-11T08:27:58Z
dc.date.issued 2019
dc.identifier.citation Bogdanov D, Porter A, Schreiber H, Urbano J, Oramas S. The AcousticBrainz genre dataset: Multi-source, multi-level, multi-label, and large-scale. In: Proceedings of the 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019): 2019 Nov 4-8; Delft, The Netherlands. [Canada]: ISMIR; 2019. p. 360-7.
dc.identifier.uri http://hdl.handle.net/10230/41985
dc.description Comunicació presentada a: 20th International Society for Music Information Retrieval Conference celebrat del 4 al 8 de novembre de 2019 a Delft, Països Baixos.
dc.description.abstract This paper introduces the AcousticBrainz Genre Dataset, a large-scale collection of hierarchical multi-label genre annotations from different metadata sources. It allows researchers to explore how the same music pieces are annotated differently by different communities following their own genre taxonomies, and how this could be addressed by genre recognition systems. Genre labels for the dataset are sourced from both expert annotations and crowds, permitting comparisons between strict hierarchies and folksonomies. Music features are available via the Acoustic- Brainz database. To guide research, we suggest a concrete research task and provide a baseline as well as an evaluation method. This task may serve as an example of the development and validation of automatic annotation algorithms on complementary datasets with different taxonomies and coverage. With this dataset, we hope to contribute to developments in content-based music genre recognition as well as cross-disciplinary studies on genre metadata analysis.
dc.description.sponsorship This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 688382 (AudioCommons) and 770376- 2 (TROMPA), as well as the Ministry of Economy and Competitiveness of the Spanish Government (Reference: TIN2015-69935-P).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher International Society for Music Information Retrieval (ISMIR)
dc.relation.ispartof Proceedings of the 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019): 2019 Nov 4-8; Delft, The Netherlands. [Canada]: ISMIR; 2019.
dc.relation.isreferencedby https://mtg.github.io/acousticbrainz-genre-dataset/#downloads
dc.rights © Dmitry Bogdanov, Alastair Porter, Hendrik Schreiber, Julián Urbano, Sergio Oramas. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Dmitry Bogdanov, Alastair Porter, Hendrik Schreiber, Julián Urbano, Sergio Oramas. “The AcousticBrainz Genre Dataset: Multi-Source, Multi-Level, Multi-Label, and Large-Scale”, 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands, 2019.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title The AcousticBrainz genre dataset: Multi-source, multi-level, multi-label, and large-scale
dc.type info:eu-repo/semantics/conferenceObject
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688382
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/770376
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-69935-P
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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