Neural Grey-Box Guitar Amplifier Modelling with Limited Data

Neural Grey-Box Guitar Amplifier Modelling with Limited Data

Paper in proceedings (conference paper)

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.

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.

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

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

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

2024

07.09.2023

Aalborg University of Copenhagen

Kodaň

Proceedings of the 25th International Conference on Digital Audio Effects (DAFx23)

2413-6689

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

Republic of Austria

8

http://hdl.handle.net/