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.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.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.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.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 © 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.accessRights info:eu-repo/semantics/openAccess
• 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.type.version info:eu-repo/semantics/publishedVersion