Unsupervised contrastive learning of sound event representations

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• dc.contributor.author Fonseca, Eduardo
• dc.contributor.author Ortego, Diego
• dc.contributor.author McGuinness, Kevin
• dc.contributor.author O’Connor, Noel E.
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2023-03-07T08:09:17Z
• dc.date.available 2023-03-07T08:09:17Z
• dc.date.issued 2021
• dc.description Comunicació presentada a 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), celebrat del 6 a l'11 de juny de 2021 de manera virtual.
• dc.description.abstract Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data—a common scenario in sound event research. In this work, we explore unsupervised contrastive learning as a way to learn sound event representations. To this end, we propose to use the pretext task of contrasting differently augmented views of sound events. The views are computed primarily via mixing of training examples with unrelated backgrounds, followed by other data augmentations. We analyze the main components of our method via ablation experiments. We evaluate the learned representations using linear evaluation, and in two in-domain downstream sound event classification tasks, namely, using limited manually labeled data, and using noisy labeled data. Our results suggest that unsupervised contrastive pre-training can mitigate the impact of data scarcity and increase robustness against noisy labels.
• dc.format.mimetype application/pdf
• dc.identifier.citation Fonseca E, Ortego D, McGuinness K, O’Connor NE, Serra X. Unsupervised contrastive learning of sound event representations. In: 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021): proceedings; 2021 Jun 6-11; Toronto, Canada. [Piscataway]: IEEE, 2021. p. 371-5. DOI: 10.1109/ICASSP39728.2021.9415009
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP39728.2021.9415009
• dc.identifier.issn 1520-6149
• dc.identifier.uri http://hdl.handle.net/10230/56076
• 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 (ICASSP 2021): proceedings; 2021 Jun 6-11; Toronto, Canada. [Piscataway]: IEEE, 2021. p. 371-5.
• 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/ICASSP39728.2021.9415009
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword contrastive learning
• dc.subject.keyword sound event classification
• dc.subject.keyword audio representation learning
• dc.subject.keyword self-supervision
• dc.title Unsupervised contrastive learning of sound event representations
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion