Reverse Engineering Memoryless Distortion Effects with Differentiable Waveshapers

Colonel JT, Comunità M, Reiss J. Reverse Engineering Memoryless Distortion Effects with Differentiable Waveshapers. In: AES Convention 153. Audio Engineering Society; 2022. Paper 10626. Available from: https://aes.org/publications/elibrary-page/?id=21955

@inproceedings{Colonel2022_21955,
  author = {Colonel, Joseph T. and Comunità, Marco and Reiss, Joshua},
  title = {{Reverse Engineering Memoryless Distortion Effects with Differentiable Waveshapers}},
  booktitle = {AES Convention 153},
  note = {Paper 10626},
  year = {2022},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21955}
}

TY  - CPAPER
TI  - Reverse Engineering Memoryless Distortion Effects with Differentiable Waveshapers
AU  - Colonel, Joseph T.
AU  - Comunità, Marco
AU  - Reiss, Joshua
T2  - AES Convention 153
M1  - Paper 10626
PY  - 2022
DA  - 2022/10/06
UR  - https://aes.org/publications/elibrary-page/?id=21955
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -