Adversarial Guitar Amplifier Modelling with Unpaired Data

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Journal Title
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Conference article in proceedings
Date
2023-06-10
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Mcode
Degree programme
Language
en
Pages
5
1-5
Series
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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.
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Keywords
Training, Computational modeling, Neural networks, Data models, Real-time systems, Recording, Timbre
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Citation
Wright , A , Välimäki , V & Juvela , L 2023 , Adversarial Guitar Amplifier Modelling with Unpaired Data . in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . , 10094600 , Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing , IEEE DataPort , pp. 1-5 , IEEE International Conference on Acoustics, Speech, and Signal Processing , Rhodes Island , Greece , 04/06/2023 . https://doi.org/10.1109/ICASSP49357.2023.10094600