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 |