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MIKLÁNEK, Š. WRIGHT, A. VÄLIMÄKI, V. SCHIMMEL, J.
Originální název
Neural Grey-Box Guitar Amplifier Modelling with Limited Data
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
guitar amplifier modelling; grey-box modelling; recurrent neural networks; virtual analogue; discretisation; state-space model
Autoři
MIKLÁNEK, Š.; WRIGHT, A.; VÄLIMÄKI, V.; SCHIMMEL, J.
Vydáno
7. 9. 2023
Nakladatel
Aalborg University of Copenhagen
Místo
Kodaň
ISSN
2413-6689
Periodikum
Proceedings of the International Conference on Digital Audio Effects (DAFx)
Stát
Rakouská republika
Strany počet
8
BibTex
@inproceedings{BUT184290, author="Štěpán {Miklánek} and Alec {Wright} and Vesa {Välimäki} and Jiří {Schimmel}", title="Neural Grey-Box Guitar Amplifier Modelling with Limited Data", booktitle="Proceedings of the 25th International Conference on Digital Audio Effects (DAFx23)", year="2023", journal="Proceedings of the International Conference on Digital Audio Effects (DAFx)", pages="8", publisher="Aalborg University of Copenhagen", address="Kodaň", issn="2413-6689" }