Fast Algorithms for Black Box Modelling of Static Hammerstein Nonlinearities

Arora S, Bennett C. Fast Algorithms for Black Box Modelling of Static Hammerstein Nonlinearities. In: AES Convention 153. Audio Engineering Society; 2022. Paper 10632. Available from: https://aes.org/publications/elibrary-page/?id=21961

@inproceedings{Arora2022_21961,
  author = {Arora, Satyarth and Bennett, Christopher},
  title = {{Fast Algorithms for Black Box Modelling of Static Hammerstein Nonlinearities}},
  booktitle = {AES Convention 153},
  note = {Paper 10632},
  year = {2022},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21961}
}

TY  - CPAPER
TI  - Fast Algorithms for Black Box Modelling of Static Hammerstein Nonlinearities
AU  - Arora, Satyarth
AU  - Bennett, Christopher
T2  - AES Convention 153
M1  - Paper 10632
PY  - 2022
DA  - 2022/10/06
UR  - https://aes.org/publications/elibrary-page/?id=21961
PB  - Audio Engineering Society
LA  - en
AB  - Emulation of Hammerstein nonlinearities using Volterra series is a popular approach for black box modelling of digital and analog nonlinearities. A simplified nonlinear Volterra model (SNVM) is a method of capturing the behaviour of a static nonlinear system. Emulation of the nonlinearity using SNVM on band–limited signals introduces aliasing. Antiderivative antialiasing (ADAA) when applied to band–limited signals is a novel approach of achieving antialiasing for SNVM, however it is computationally intensive to be practical for real time applications. In this paper, we use different optimization strategies to reduce the complexity of the algorithm and achieve faster than real-time black box modelling of static Hammerstein nonlinearities.
ER  -