Detail publikace

Neural Grey-Box Guitar Amplifier Modelling with Limited Data

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"
}