Antialiasing for Simplified Nonlinear Volterra Models

Bennett C, Hopman S. Antialiasing for Simplified Nonlinear Volterra Models. J Audio Eng Soc. 2022;70(9):690-697. doi:10.17743/jaes.2022.0033

@article{Bennett2022_21882,
  author = {Bennett, Christopher and Hopman, Stefan},
  title = {{Antialiasing for Simplified Nonlinear Volterra Models}},
  journal = {Journal of the Audio Engineering Society},
  volume = {70},
  number = {9},
  pages = {690--697},
  year = {2022},
  month = sep,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0033},
  url = {https://doi.org/10.17743/jaes.2022.0033}
}

TY  - JOUR
TI  - Antialiasing for Simplified Nonlinear Volterra Models
AU  - Bennett, Christopher
AU  - Hopman, Stefan
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 70
IS  - 9
SP  - 690
EP  - 697
PY  - 2022
DA  - 2022/09/06
DO  - 10.17743/jaes.2022.0033
UR  - https://doi.org/10.17743/jaes.2022.0033
PB  - Audio Engineering Society
LA  - en
AB  - Antiderivative antialiasing (ADAA) has emerged as a recent approach to reduce aliases for mathematically defined nonlinearities. In this study, ADAA is applied to simplified nonlinear Volterra modeling, which is a method for blackbox modeling of Hammerstein nonlinearities. Previously reported ADAA approaches contain a variable difference term in the denominator and therefore rely on a continuous piecewise function to prevent very small denominators. However, when applied to simplified Volterra models, this denominator term is eliminated, resulting in a polynomial function. This polynomial ADAA was tested against the standard approach of low-pass filtering the input to prevent aliasing. It was found that these two approaches perform comparably but that by combining them together, superior alias reduction can be achieved.
ER  -