Abstract
This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.
Highlights
Many popular guitar amplifiers and distortion effects are based on analog circuitry
This paper focuses on a model we proposed [23] for nonlinear cicuit black-box modelling, that was based on the WaveNet convolutional neural network [24]
As there is an interest in the real-time performance of the models, the validation loss is shown as a function of the processing speed on the developed real-time C++ implementation of the model
Summary
Many popular guitar amplifiers and distortion effects are based on analog circuitry. To achieve the desired distortion of the guitar signal, these circuits use nonlinear components, such as vacuum tubes, diodes, or transistors. As music production becomes increasingly digitised, the demand for faithful digital emulations of analog audio effects is increasing [1,2,3]. The main objective of the field of Virtual Analog (VA) modelling is to create digital emulations of these analog systems. This allows bulky, expensive and fragile analog equipment to be replaced by software plugins that can be used on a modern desktop computer. A common approach for VA modelling of distortion effects is “white-box” modelling [4,5,6,7]
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