Adversarial Guitar Amplifier Modelling with Unpaired Data

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

6 Citations (Scopus)
43 Downloads (Pure)

Abstract

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 trans-forming 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 un-paired 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.
Original languageEnglish
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-7281-6327-7
ISBN (Print)978-1-7281-6328-4
DOIs
Publication statusPublished - 10 Jun 2023
MoE publication typeA4 Conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
Country/TerritoryGreece
CityRhodes Island
Period04/06/202310/06/2023

Keywords

  • Training
  • Computational modeling
  • Neural networks
  • Data models
  • Real-time systems
  • Recording
  • Timbre

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