Evaluating neural networks architectures for spring reverb modelling

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• dc.contributor.author Papaleo, Francesco
• dc.contributor.author Lizarraga-Seijas, Xavier
• dc.contributor.author Font, Frederic
• dc.date.accessioned 2024-10-15T06:15:13Z
• dc.date.available 2024-10-15T06:15:13Z
• dc.date.issued 2024
• dc.description.abstract Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.
• dc.format.mimetype application/pdf
• dc.identifier.citation Papaleo F, Lizarraga-Seijas X, Font F. Evaluating neural networks architectures for spring reverb modelling. In: Sena E, Mannall J, editors. Proceedings of the 27th International Conference on Digital Audio Effects (DAFx24); 2024 Sep 3-7; Guildford, United Kingdom. [Guildford]: University of Surrey; 2024. p. 302-9.
• dc.identifier.uri http://hdl.handle.net/10230/61394
• dc.language.iso eng
• dc.rights Copyright: © 2024 Francesco Papaleo, Xavier Lizarraga-Seijas and Frederic Font. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, adaptation, and reproduction in any medium, provided the original author and source are credited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Neural networks architectures
• dc.subject.keyword Spring reverb modelling
• dc.title Evaluating neural networks architectures for spring reverb modelling
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion