# Adversarial Guitar Amplifier Modelling With Unpaired Data

arXiv: https://arxiv.org/abs/2211.00943 · HTML: https://mlsanity.com/paper/2211.00943/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2022.

## Built on by (most cited first, 5 of 5)

- [GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures](https://mlsanity.com/paper/2509.22655/) (2025)
- [Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches](https://mlsanity.com/paper/2504.04751/) (2025)
- [Neural modeling of magnetic tape recorders](https://mlsanity.com/paper/2305.16862/) (2023)
- [Gaussian Flow Bridges for Audio Domain Transfer with Unpaired Data](https://mlsanity.com/paper/2405.19497/) (2024)
- [Time-Varying Audio Effect Modeling by End-to-End Adversarial Training](https://mlsanity.com/paper/2512.15313/) (2025)

## Abstract (arXiv)

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, with the goal of transforming the timbre of a guitar, into the timbre of another guitar after audio effects processing has been applied, for example, by a guitar amplifier. The model training requires no paired data, and the resulting model emulates the target timbre well whilst being capable of real-time processing on a modern personal computer. To verify our approach we present two experiments, one which carries out unpaired training using paired data, allowing us to monitor training via objective metrics, and another that uses fully unpaired data, corresponding to a realistic scenario where a user wants to emulate a guitar timbre only using audio data from a recording. Our listening test results confirm that the models are perceptually convincing.