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We present a lightweight method of reverse engineering distortion effects using Wiener-Hammerstein models implemented in a differentiable framework. The Wiener-Hammerstein models are formulated using graphic equalizer pre-emphasis and de-emphasis filters and a parameterized waveshaping function. Several parameterized waveshaping functions are proposed and evaluated. The performance of each method is measured both objectively and subjectively on a dataset of guitar distortion emulation software plugins and guitar audio samples.
Author (s): Colonel, Joseph T.; Comunità, Marco; Reiss, Joshua
Affiliation:
Queen Mary University of London, UK; Queen Mary University of London, UK; Queen Mary University of London, UK
(See document for exact affiliation information.)
AES Convention: 153
Paper Number:10626
Publication Date:
2022-10-06
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Session subject:
Signal Processing
Permalink: https://aes2.org/publications/elibrary-page/?id=21955
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Colonel, Joseph T.; Comunità, Marco; Reiss, Joshua; 2022; Reverse Engineering Memoryless Distortion Effects with Differentiable Waveshapers [PDF]; Queen Mary University of London, UK; Queen Mary University of London, UK; Queen Mary University of London, UK; Paper 10626; Available from: https://aes2.org/publications/elibrary-page/?id=21955
Colonel, Joseph T.; Comunità, Marco; Reiss, Joshua; Reverse Engineering Memoryless Distortion Effects with Differentiable Waveshapers [PDF]; Queen Mary University of London, UK; Queen Mary University of London, UK; Queen Mary University of London, UK; Paper 10626; 2022 Available: https://aes2.org/publications/elibrary-page/?id=21955