Melon playlist dataset: a public dataset for audio-based playlist generation and music tagging

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• dc.contributor.author Ferraro, Andrés
• dc.contributor.author Kim, Yuntae
• dc.contributor.author Lee, Soohyeon
• dc.contributor.author Kim, Biho
• dc.contributor.author Jo, Namjun
• dc.contributor.author Lim, Semi
• dc.contributor.author Lim, Suyon
• dc.contributor.author Jang, Jungtaek
• dc.contributor.author Kim, Sehwan
• dc.contributor.author Serra, Xavier
• dc.contributor.author Bogdanov, Dmitry
• dc.date.accessioned 2023-03-09T07:26:49Z
• dc.date.issued 2021
• dc.description Comunicació presentada a 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), celebrat del 6 a l'11 de juny de 2021 de manera virtual.
• dc.description.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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ferraro A, Kim Y, Lee S, Kim B, Jo N, Lim S, Lim S, Jang J, Kim S, Serra X, Bogdanov D. Melon playlist dataset: a public dataset for audio-based playlist generation and music tagging. In: 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing: proceedings; 2021 Jun 6-11; [Piscataway]: IEEE; 2021. p. 536-40. DOI: 10.1109/ICASSP39728.2021.9413552
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP39728.2021.9413552
• dc.identifier.issn 1520-6149
• dc.identifier.uri http://hdl.handle.net/10230/56126
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing: proceedings; 2021 Jun 6-11; [Piscataway]: IEEE; 2021. p. 536-40.
• dc.relation.isreferencedby https://github.com/andrebola/icassp2021
• dc.rights © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/ICASSP39728.2021.9413552
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword datasets
• dc.subject.keyword music information retrieval
• dc.subject.keyword music playlists
• dc.subject.keyword auto-tagging
• dc.subject.keyword audio signal processing
• dc.title Melon playlist dataset: a public dataset for audio-based playlist generation and music tagging
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion