Neural Grey-Box Guitar Amplifier Modelling With Limited Data

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openAccess

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Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2023-09-04

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Language

en

Pages

8
151-158

Series

Proceedings of the 26th International Conference on Digital Audio Effects (DAFx23), Proceedings of the International Conference on Digital Audio Effects

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.

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Keywords

Audio signal processing, deep learning, digital filter design

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Citation

Miklanek, S, Wright, A, Välimäki, V & Schimmel, J 2023, Neural Grey-Box Guitar Amplifier Modelling With Limited Data . in F Fontana & S Willemsen (eds), Proceedings of the 26th International Conference on Digital Audio Effects (DAFx23) . Proceedings of the International Conference on Digital Audio Effects, Aalborg University, Copenhagen, Denmark, pp. 151-158, International Conference on Digital Audio Effects, Copenhagen, Denmark, 04/09/2023 .