Year: 2022
Type: article
Abstract: Vacuum 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.
Source: Applied Sciences
DOI: 10.3390/app12125894
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Bordeaux, Laboratoire Bordelais de Recherche en Informatique, Institut Polytechnique de Bordeaux, Institut de Mathématiques de Bordeaux, ASTRAL: Méthodes avancées d'apprentissage statistique et de contrôle
Language: English
Subfield: Music
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,408
OpenAlex ID: W4281785064