Reverse Engineering Memoryless Distortion Effects with Differentiable Waveshapers

Colonel, J. T., Comunità, M., Reiss, J. D. - AES Convention - 153 (October 2022)

Abstract

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.

Resources

• Paper: https://www.aes.org/e-lib/browse.cfm?elib=21955
• Webpage: https://jtcolonel.github.io/DistRevEng
• Presentation: colonel2022waveshapers-presentation.pdf

Citation

Colonel, J. T., Comunità, M., Reiss, J. D. "Reverse Engineering Memoryless Distortion Effects with Differentiable Waveshapers" - Audio Engineering Society Convention 153. October, 2022.
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