This repository contains supplementary material for the DAFx23 paper Neural Grey-Box Guitar Amplifier Modelling with Limited Data.
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.
The plugin directory contains the source code for the real-time version of the models presented in this work. The VST3 version compiled on Windows is located in the plugin/windows_build folder. The plugin enables switching between the proposed models (denoted as TS) trained on 4 and 12 minutes of data. The third model (RNN21) is a fully black-box conditioned LSTM model trained on 84 minutes of data.
@inproceedings{miklanek2023greybox,
title={Neural Grey-Box Guitar Amplifier Modelling with Limited Data},
author={Š. Miklánek and A. Wright and V. Välimäki and J. Schimmel},
booktitle = {Proceedings of the International Conference on Digital Audio Effects (DAFx23)},
year = {2023},
address = {Copenhagen, Denmark},
month = {Sep.}
}