Dataset Open Access

Contextual Tags for music auto-tagging

Karim M. Ibrahim; Jimena Royo-Letelier; Elena V. Epure; Geoffroy Peeters; Gaël Richard

The dataset is composed of 15 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, chill, club, dance, gym, happy, night, party, relax, running, sad, sleep, summer, work, workout". For each  track one or multiple tags are associated with it indicating that users listen to the track in the associated context. 

The creation of the dataset and the initial baseline of an auto-tagging model is described in the paper: Ibrahim, Karim M., Jimena Royo-Letelier, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "AUDIO-BASED AUTO-TAGGING WITH CONTEXTUAL TAGS FOR MUSIC." 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.

The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api 

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Context_tags_dataset.csv
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