Neural Grey-Box Guitar Amplifier Modelling with Limited Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148735" target="_blank" >RIV/00216305:26220/23:PU148735 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Neural Grey-Box Guitar Amplifier Modelling with Limited Data
Original language description
This paper combines recurrent neural networks (RNNs) with the discretised Kirchhoff nodal analysis (DK-method) to create a grey-box guitar amplifier model. Both the objective and subjective results suggest that the proposed model is able to outperform a baseline black-box RNN model in the task of modelling a guitar amplifier, including realistically recreating the behaviour of the amplifier equaliser circuit, whilst requiring significantly less training data. Furthermore, we adapt the linear part of the DK-method in a deep learning scenario to derive multiple state-space filters simultaneously. We frequency sample the filter transfer functions in parallel and perform frequency domain filtering to considerably reduce the required training times compared to recursive state-space filtering. This study shows that it is a powerful idea to separately model the linear and nonlinear parts of a guitar amplifier using supervised learning.
Czech name
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Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 25th International Conference on Digital Audio Effects (DAFx23)
ISBN
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ISSN
2413-6689
e-ISSN
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Number of pages
8
Pages from-to
„“-„“
Publisher name
Aalborg University of Copenhagen
Place of publication
Kodaň
Event location
Kodaň
Event date
Sep 4, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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