Dataset Open Access

MediaEval AcousticBrainz Genre

Bogdanov, Dmitry; Porter, Alastair; Urbano, Julián; Schreiber, Hendrik

The MediaEval AcousticBrainz Genre datasets are datasets of genre annotations and music features extracted from audio suited for evaluation of hierarchical multi-label genre classification systems.

The datasets are used within the MediaEval AcousticBrainz Genre Task. The task is focused on content-based music
genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems.

We provide four datasets containing genre and subgenre annotations extracted from four different online metadata sources:

  • AllMusic and Discogs are based on editorial metadata databases maintained by music experts and enthusiasts. These sources contain explicit genre/subgenre annotations of music releases (albums) following a predefined genre namespace and taxonomy. We propagated release-level annotations to recordings (tracks) in AcousticBrainz to build the datasets.

  • Lastfm and Tagtraum are based on collaborative music tagging platforms with large amounts of genre labels provided by their users for music recordings (tracks). We have automatically inferred a genre/subgenre taxonomy and annotations from these labels.

For details on format and contents, please refer to the data webpage.

Note, that the AllMusic ground-truth annotations are distributed separately at https://zenodo.org/record/2554044.

 

Citation

If you use the MediaEval AcousticBrainz Genre dataset or part of it, please cite our MediaEval 2018 overview paper:

Bogdanov D, Porter A, Urbano J, Schreiber H. The MediaEval 2018 AcousticBrainz genre task: Content-based music genre recognition from multiple sources. Paper presented at: MediaEval'18; 2018 Oct 29-31; Sophia Antipolis, France.

 

Acknowledgements

This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 AudioCommons.

 

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