The AcousticBrainz Genre Dataset: Music Genre Recognition with Annotations from Multiple Sources

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 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.
Original languageEnglish
Title of host publicationInternational Society for Music Information Retrieval Conference
Pages360-367
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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  • Cite this

    Bogdanov, D., Porter, A., Schreiber, H., Urbano, J., & Oramas, S. (2019). The AcousticBrainz Genre Dataset: Music Genre Recognition with Annotations from Multiple Sources. In International Society for Music Information Retrieval Conference (pp. 360-367)