Data-driven harmonic filters for audio representation learning

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• dc.contributor.author Won, Minz
• dc.contributor.author Chun, Sanghyuk
• dc.contributor.author Nieto Caballero, Oriol
• dc.date.accessioned 2020-04-20T08:26:16Z
• dc.date.issued 2020
• dc.description Comunicació presentada a: ICASSP 2020 IEEE InternationalConference on Acoustics, Speech,and Signal Processing, celebrat en línia del 4 al 8 de maig de 2020.
• dc.description.abstract We introduce a trainable front-end module for audio representation learning that exploits the inherent harmonic structure of audio signals. The proposed architecture, composed of a set of filters, compels the subsequent network to capture harmonic relations while preserving spectro-temporal locality. Since the harmonic structure is known to have a key role in human auditory perception, one can expect these harmonic filters to yield more efficient audio representations. Experimental results show that a simple convolutional neural network back-end with the proposed front-end outperforms state-of-the-art baseline methods in automatic music tagging, keyword spotting, and sound event tagging tasks.en
• dc.description.sponsorship This work was funded by the predoctoral grant MDM-2015- 0502-17-2 from the Spanish Ministry of Economy and Competitiveness linked to the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
• dc.format.mimetype application/pdf
• dc.identifier.citation Won M, Chun S, Nieto O, Serra X. Data-driven harmonic filters for audio representation learning. In: 2020 IEEE InternationalConference on Acoustics, Speech,and Signal Processing Proceedings; 2020 May 4-8; Barcelona, Spain. [New York]: IEEE; 2020. p. 536-40. DOI: 10.1109/ICASSP40776.2020.9053669
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP40776.2020.9053669
• dc.identifier.isbn 978-1-5090-6631-5
• dc.identifier.issn 2379-190X
• dc.identifier.uri http://hdl.handle.net/10230/44278
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2020 IEEE InternationalConference on Acoustics, Speech,and Signal Processing Proceedings; 2020 May 4-8; Barcelona, Spain. [New York]: IEEE; 2020. p. 536-40.
• dc.rights © 2020 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/ICASSP40776.2020.9053669
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Harmonic filtersen
• dc.subject.keyword Audio representation learningen
• dc.subject.keyword Deep learningen
• dc.title Data-driven harmonic filters for audio representation learning
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion