2026-04-14T04:23:07Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/419852025-12-26T11:26:50Zcom_2072_6col_2072_452952
RECERCAT
author
Bogdanov, Dmitry
author
Porter, Alastair
author
Schreiber, Hendrik
author
Urbano, Julián
author
Oramas, Sergio
2019-07-11T08:27:58Z2019-07-11T08:27:58Z2019
Comunicació presentada a: 20th International Society for Music Information Retrieval Conference celebrat del 4 al 8 de novembre de 2019 a Delft, Països Baixos.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.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).
© 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. https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess
The AcousticBrainz genre dataset: Multi-source, multi-level, multi-label, and large-scale
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