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 ...
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.
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