Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging
This dataset addresses a problem for researchers in music information retrieval by providing a public resource to overcome copyright restrictions and support work on cold-start issues, though it is incremental as it builds on existing data collection efforts.
The authors tackled the lack of large public datasets for audio signal processing by creating the Melon Playlist Dataset, which includes mel-spectrograms for 649,091 tracks and 148,826 playlists annotated with 30,652 tags, enabling tasks like auto-tagging and automatic playlist continuation.
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.