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