THE ACOUSTICBRAINZ GENRE DATASET
MULTI-SOURCE, MULTI-LEVEL, MULTI-LABEL, AND LARGE-SCALE
Dmitry Bogdanov (Pompeu Fabra University)
Alastair Porter (Pompeu Fabra University)
Hendrik Schreiber (Tagtraum Industries Incorporated)
Julián Urbano (TU Delft - Multimedia Computing)
Sergio Oramas (Pandora)
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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 AcousticBrainz 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
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