Using Volterra Series Modeling Techniques to Classify Black-Box Audio Effects

Hoerr E, Maher RC. Using Volterra Series Modeling Techniques to Classify Black-Box Audio Effects. In: AES Convention 147. Audio Engineering Society; 2019. Paper 10225. Available from: https://aes.org/publications/elibrary-page/?id=20598

@inproceedings{Hoerr2019_20598,
  author = {Hoerr, Ethan and Maher, Robert C.},
  title = {{Using Volterra Series Modeling Techniques to Classify Black-Box Audio Effects}},
  booktitle = {AES Convention 147},
  note = {Paper 10225},
  year = {2019},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20598}
}

TY  - CPAPER
TI  - Using Volterra Series Modeling Techniques to Classify Black-Box Audio Effects
AU  - Hoerr, Ethan
AU  - Maher, Robert C.
T2  - AES Convention 147
M1  - Paper 10225
PY  - 2019
DA  - 2019/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20598
PB  - Audio Engineering Society
LA  - en
AB  - Digital models of various audio devices are useful for simulating audio processing effects, but developing good models of nonlinear systems can be challenging. This paper reports on the in-progress work of determining attributes of black-box audio devices using Volterra series modeling techniques. In general, modeling an audio effect requires determination of whether the system is linear or nonlinear, time-invariant or –variant, and whether it has memory. For nonlinear systems, we must determine the degree of nonlinearity of the system, and the required parameters of a suitable model. We explain our work in making educated guesses about the order of nonlinearity in a memoryless system and then discuss the extension to nonlinear systems with memory.
ER  -