Publication detail

Neural Grey-Box Guitar Amplifier Modelling with Limited Data

MIKLÁNEK, Š. WRIGHT, A. VÄLIMÄKI, V. SCHIMMEL, J.

Original Title

Neural Grey-Box Guitar Amplifier Modelling with Limited Data

Type

conference paper

Language

English

Original Abstract

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.

Keywords

guitar amplifier modelling; grey-box modelling; recurrent neural networks; virtual analogue; discretisation; state-space model

Authors

MIKLÁNEK, Š.; WRIGHT, A.; VÄLIMÄKI, V.; SCHIMMEL, J.

Released

7. 9. 2023

Publisher

Aalborg University of Copenhagen

Location

Kodaň

ISBN

2413-6689

Periodical

Proceedings of the International Conference on Digital Audio Effects (DAFx)

State

Republic of Austria

Pages count

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"
}