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 …

Virtual analog modeling of audio effects consists of emulating the sound of an audio
processor reference device. This digital simulation is normally done by designing …

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 …

This paper introduces Open-Amp, a synthetic data framework for generating large-scale and
diverse audio effects data. Audio effects are relevant to many musical audio processing and …

Machine learning approaches to modelling analog audio effects have seen intensive
investigation in recent years, particularly in the context of non-linear time-invariant effects …

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 …

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 …

Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus,
modify an input signal using time-varying filters and delays, resulting in characteristic …

Audio processors whose parameters are modified periodically over time are often referred
as time-varying or modulation based audio effects. Most existing methods for modeling …