  One of the main limitations in the field of audio signal processing is the
lack of large public datasets with audio representations and high-quality
annotations due to restrictions of copyrighted commercial music. We present
Melon Playlist Dataset, a public dataset of mel-spectrograms for 649,091tracks
and 148,826 associated playlists annotated by 30,652 different tags. All the
data is gathered from Melon, a popular Korean streaming service. The dataset is
suitable for music information retrieval tasks, in particular, auto-tagging and
automatic playlist continuation. Even though the latter can be addressed by
collaborative filtering approaches, audio provides opportunities for research
on track suggestions and building systems resistant to the cold-start problem,
for which we provide a baseline. Moreover, the playlists and the annotations
included in the Melon Playlist Dataset make it suitable for metric learning and
representation learning.
