THE ACOUSTICBRAINZ GENRE DATASET: MULTI-SOURCE, MULTI-LEVEL, MULTI-LABEL, AND LARGE-SCALE

Dmitry Bogdanov, Alastair Porter, Hendrik Schreiber, Julián Urbano, Sergio Oramas

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientific

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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
Original languageEnglish
Title of host publicationInternational Society for Music Information Retrieval Conference 2019
Pages360-367
Number of pages8
Publication statusPublished - 2019
EventISMIR 2019: 20th International Society for Music Information Retrieval Conference - Delft, Netherlands
Duration: 4 Nov 20198 Nov 2019
Conference number: 20th

Conference

ConferenceISMIR 2019
CountryNetherlands
CityDelft
Period4/11/198/11/19

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    Bogdanov, D., Porter, A., Schreiber, H., Urbano, J., & Oramas, S. (2019). THE ACOUSTICBRAINZ GENRE DATASET: MULTI-SOURCE, MULTI-LEVEL, MULTI-LABEL, AND LARGE-SCALE. In International Society for Music Information Retrieval Conference 2019 (pp. 360-367)