Real-Time Guitar Amplifier Emulation with Deep Learning

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.

Lightweight and Interpretable Neural Modeling of an Audio Distortion Effect Using Hyperconditioned Differentiable Biquads

“…In addition to the proposed models, we train a feedforward variant of WaveNet [7], serving as a baseline black-box model. The 1st layer of the model is a 1 × 1 convolutional layer outputting 16 channels, followed by 10 dilated convolutional layers, with dilation rate increasing by a factor of 2 at each layer.…”

Automatic Multitrack Mixing With A Differentiable Mixing Console Of Neural Audio Effects

“…Various deep learning approaches have already been proposed for the task of modeling audio effects [12][13][14][15][16][17]. While previous approaches have focused on training a single model for each effect, we believe our work is the first to consider building a model that emulates a series connection of effects and their parameters, jointly.…”

“…While previous approaches have focused on training a single model for each effect, we believe our work is the first to consider building a model that emulates a series connection of effects and their parameters, jointly. Most approaches do not consider modeling the different configurations of these devices, and those that do, only consider a sparse sampling of the parameters [14,16]. This is due to the fact that they aim to emulate an analog device, and the process of collecting data at many configurations is often impractical.…”

A Deep Learning Approach to Intelligent Drum Mixing With the Wave-U-Net

“…Martínez Ramírez et al [24,25,26,27] investigated DNNs for audio processing tasks, such as modeling of various types of audio effects. Similarly, Wright et al [28] explored variants of the WaveNet architecture [29] and recurrent neural networks to model distortion audio effects. Hawley et al [30] proposed a DNN based on U-Net [31] and Time-Frequency [32] networks to model DRC.…”