Welcome to the UPF Digital Repository

Pre-Training Strategies Using Contrastive Learning and Playlist Information for Music Classification and Similarity

Show simple item record

dc.contributor.author Alonso-Jiménez, Pablo
dc.contributor.author Favory, Xavier
dc.contributor.author Foroughmand, Hadrien
dc.contributor.author Bourdalas, Grigoris
dc.contributor.author Serra, Xavier
dc.contributor.author Lidy, Thomas
dc.contributor.author Bogdanov, Dmitry
dc.date.accessioned 2023-04-25T13:42:18Z
dc.date.available 2023-04-25T13:42:18Z
dc.date.issued 2023-04-25
dc.identifier.uri http://hdl.handle.net/10230/56564
dc.description This work has been accepted at the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), at Rhodes, Greece. June 4-10, 2023.
dc.description.abstract In this work, we investigate an approach that relies on contrastive learning and music metadata as a weak source of supervision to train music representation models. Recent studies show that contrastive learning can be used with editorial metadata (e.g., artist or album name) to learn audio representations that are useful for different classification tasks. In this paper, we extend this idea to using playlist data as a source of music similarity information and investigate three approaches to generate anchor and positive track pairs. We evaluate these approaches by fine-tuning the pre-trained models for music multi-label classification tasks (genre, mood, and instrument tagging) and music similarity. We find that creating anchor and positive track pairs by relying on cooccurrences in playlists provides better music similarity and competitive classification results compared to choosing tracks from the same artist as in previous works. Additionally, our best pre-training approach based on playlists provides superior classification performance for most datasets.
dc.description.sponsorship This research was partially funded by Musical AI - PID2019-111403GBI00/ AEI/10.13039/501100011033 of the Spanish Ministerio de Ciencia, Innovacion y Universidades
dc.format.mimetype application/pdf
dc.language.iso eng
dc.title Pre-Training Strategies Using Contrastive Learning and Playlist Information for Music Classification and Similarity
dc.type info:eu-repo/semantics/preprint
dc.subject.keyword music representation learning
dc.subject.keyword contrastive learning
dc.subject.keyword music classification
dc.subject.keyword music similarity
dc.subject.keyword pre-training neural networks
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GBI00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/submittedVersion

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

In collaboration with Compliant to Partaking