This paper proposes to use a recurrent neural network for blackbox modelling of nonlinear
audio systems, such as tube amplifiers and distortion pedals. As a recurrent unit structure …

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 …

Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities.
Faithfully modeling such significant aspect of the original sound in virtual analog software …

This paper studies deep neural networks for modeling of audio distortion circuits. The
selected approach is blackbox modeling, which estimates model parameters based on the …

Recurrent neural networks (RNNs) have demonstrated impressive results for virtual analog
modeling of audio effects. These networks process time-domain audio signals using a series …

This work investigates alternate pre-emphasis filters used as part of the loss function during
neural network training for nonlinear audio processing. In our previous work, the error-to …

Deep learning approaches have demonstrated success in the task of modeling analog
audio effects such as distortion and overdrive. Nevertheless, challenges remain in modeling …

We present a lightweight method of reverse engineering distortion effects using Wiener-
Hammerstein models implemented in a differentiable framework. The Wiener-Hammerstein …

We propose an audio effects processing framework that learns to emulate a target electric
guitar tone from a recording. We train a deep neural network using an adversarial approach …

This article further explores a previously proposed gray-box neural network approach to
modeling LFO (low-frequency oscillator) modulated time-varying audio effects. The network …