2021/01/30 by Andrés Ferraro, Yun-Tae Kim, Ferraro, Andres +19 · 1 citation
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Multimedia (cs.MM) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2102.00201
openalex publication_date 2021/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the main limitations in the field of audio signal processing is the\nlack of large public datasets with audio representations and high-quality\nannotations due to restrictions of copyrighted commercial music. We present\nMelon Playlist Dataset, a public dataset of mel-spectrograms for 649,091tracks\nand 148,826 associated playlists annotated by 30,652 different tags. All the\ndata is gathered from Melon, a popular Korean streaming service. The dataset is\nsuitable for music information retrieval tasks, in particular, auto-tagging and\nautomatic playlist continuation. Even though the latter can be addressed by\ncollaborative filtering approaches, audio provides opportunities for research\non track suggestions and building systems resistant to the cold-start problem,\nfor which we provide a baseline. Moreover, the playlists and the annotations\nincluded in the Melon Playlist Dataset make it suitable for metric learning and\nrepresentation learning.\n