Paper Info
Title
MELON PLAYLIST DATASET: A PUBLIC DATASET FOR AUDIO-BASED PLAYLIST GENERATION AND MUSIC TAGGING
Abstract
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 649,091 tracks 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.
Year
DOI
Venue
2021
10.1109/ICASSP39728.2021.9413552
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Keywords
DocType
Citations 
Datasets, music information retrieval, music playlists, auto-tagging, audio signal processing
Conference
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Andres Ferraro174.64
Yuntae Kim200.68
Soohyeon Lee300.34
Biho Kim400.34
Namjun Jo500.34
Semi Lim600.34
Suyon Lim700.34
Jungtaek Jang800.34
Sehwan Kim900.34
Xavier Serra101014118.93
Dmitry Bogdanov1123620.72