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Genre Annotations

All four training genre datasets are distributed as TSV files with the following format:

[RecordingID] [ReleaseGroupID] [genre/subgenre label] [genre/subgenre label] ...

A real data example:

6bb7e980-791c-44b5-9024-cc7c90bc8230    969ebfe8-0786-3ee0-b49b-3005fe653aa4    metal   metal---heavymetal  metal---progressivemetal    rock    rock---progressiverock
92a70a47-98c4-43fd-8b1f-972657f627c3    7378d3cf-a3a9-3fe3-825b-70d6f0230250    country country---countryfolk   folk
c7bee376-0020-461a-90a7-d5af73cfff05    6e652b2f-6f94-47ef-834a-a85d25921fce    soul
93597a3e-cdca-4123-bcf5-343ff8debbe2    47de1259-bdeb-3f11-b612-4976887dca5c    pop pop---ballad
a4d017d4-e75b-4eac-8f46-1b000ef407b0    9b1640de-4eb7-3071-b6a3-1c6f76c1a1b4    electronic  electronic---ambient    electronic---downtempo  pop rock    rock---indie    rock---spacerock
27b7cf35-0238-4316-b2fd-c589a866603a    b6f21355-5e8e-33f7-acbf-03d99e9e90f9    electronic  electronic---bigbeat    electronic---techno

Each line corresponds to one recording (a music track or song), and contains all its ground-truth genre and subgenre labels. recordingmbid is the MusicBrainz identifier of the particular recording. To distinguish between genre and subgenre labels, subgenre strings are compound and contain --- as a separator between a parent genre and an actual subgenre name. For example, rock, electronic, jazz and hip hop are genres, while electronic---ambient, rock---singersongwriter and jazz---latinjazz are subgenres.

Additionally, we provide releasegroupmbid for each recording, which is a MusicBrainz identifier of a release group (an album, single, or compilation) that it belongs to. This data may be useful if one wants to avoid an “album effect” [4], which consists in potential overestimation of the performance of a classifier when a test set contains music recordings from the same albums as the training set.

Groundtruth files have a header

recordingmbid   releasegroupmbid    genre1  genre2  ... genren

to show that the first two columns contain MusicBrainz IDs, and subsequent columns contain genre annotations. As the number of annotations per recording differ, this header contains as many rows as necessary to provide a header to the row with the most annotations. Additionally, rows with fewer genre annotations are padded with the field separator (a tab) to ensure that all rows have the same number of columns. You should ensure that you remove “empty” annotations if your preferred tool to read these files does not do this automatically.

Genre annotations are ordered alphabetically. There is no correlation between the annotations of two different recordings in the same column.

Music features

We provide a dataset of music features precomputed from audio for every music recording. The dataset can be downloaded as an archive. It contains a JSON file with music features for every RecordingID. See an example JSON file.

All music features are taken from the community-built database AcousticBrainz and were extracted from audio using Essentia, an open-source library for music audio analysis [2]. They are grouped into categories (low-level, rhythm, and tonal) and are explained in detail here. Only statistical characterization of time frames is provided (bag of features), no frame-level data is available.

Development and Test Data

The development data contains:

The test data contains four archives of music features for recordings with anonymized RecordingIDs. To avoid a potential album effect [4], no recording in the test set contains music from the same release groups as the recordings in the train set.

All data is compressed with bzip2. Checksums are provided to ensure that you have correctly downloaded the archives.


The development and test data for Discogs, Lastfm and Tagtraum is now publicly available on Zenodo.

The development data (genre ground truth) and test data for AllMusic requires signing the Data Usage agreement by participants and is also available on Zenodo.


To give an idea of the scale of the data, we report some statistics for the train datasets.





Genre/subgenre taxonomy and distribution in terms of recordings and releasegroups for all four development datasets are reported here.

The datasets are partially intersected.

Note that the data we provide is very large-scale. It includes a large number of music recordings and many of music features for those recordings. Participants are free to use all of the data to train their systems or only its part.

Please, contact the organizers if you have further questions or need help.