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A Review of Neural Network-Based Emulation of Guitar Amplifiers

hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierMéthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]
dc.contributor.authorVANHATALO, Tara
hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierMéthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorLEGRAND, Pierrick
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorDESAINTE-CATHERINE, Myriam
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorHANNA, Pierre
hal.structure.identifierOrosys
dc.contributor.authorBRUSCO, Antoine
hal.structure.identifierOrosys
dc.contributor.authorPILLE, Guillaume
hal.structure.identifierCentre National de la Recherche Scientifique [CNRS]
hal.structure.identifierStudio de Création et de Recherche en Informatique et Musique Électroacoustique [SCRIME]
hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierOrosys
dc.contributor.authorBAYLE, Yann
dc.date.accessioned2024-04-04T02:37:29Z
dc.date.available2024-04-04T02:37:29Z
dc.date.issued2022-06
dc.identifier.issn2076-3417
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190798
dc.description.abstractEnVacuum tube amplifiers present sonic characteristics frequently coveted by musicians, that are often due to the distinct nonlinearities of their circuits, and accurately modelling such effects can be a challenging task. A recent rise in machine learning methods has lead to the ubiquity of neural networks in all fields of study including virtual analog modelling. This has lead to the appearance of a variety of architectures tailored to this task. This article aims to provide an overview of the current state of the research in neural emulation of analog distortion circuits by first presenting preceding methods in the field and then focusing on a complete review of the deep learning landscape that has appeared in recent years, detailing each subclass of available architectures. This is done in order to bring to light future possible avenues of work in this field.
dc.language.isoen
dc.publisherMultidisciplinary digital publishing institute (MDPI)
dc.subject.enaudio signal processing
dc.subject.ennonlinear modelling
dc.subject.endeep learning
dc.subject.enaudio effects modelling
dc.subject.envirtual analog modelling
dc.subject.enneural network
dc.subject.enmodelling nonlinear audio effects
dc.subject.endistortion effects
dc.subject.enelectric musical instruments
dc.title.enA Review of Neural Network-Based Emulation of Guitar Amplifiers
dc.typeArticle de revue
dc.identifier.doi10.3390/app12125894
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
bordeaux.journalApplied Sciences
bordeaux.page5894
bordeaux.volume12
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue12
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03881859
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03881859v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Applied%20Sciences&rft.date=2022-06&rft.volume=12&rft.issue=12&rft.spage=5894&rft.epage=5894&rft.eissn=2076-3417&rft.issn=2076-3417&rft.au=VANHATALO,%20Tara&LEGRAND,%20Pierrick&DESAINTE-CATHERINE,%20Myriam&HANNA,%20Pierre&BRUSCO,%20Antoine&rft.genre=article


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