Modeling and Adaptive Filtering for Systems with Output Nonlinearity

Soltanmohammadi E, Painter C, Jain K. Modeling and Adaptive Filtering for Systems with Output Nonlinearity. In: AES Convention 140. Audio Engineering Society; 2016. Paper 9564. Available from: https://aes.org/publications/elibrary-page/?id=18262

@inproceedings{Soltanmohammadi2016_18262,
  author = {Soltanmohammadi, Erfan and Painter, Christopher and Jain, Kapil},
  title = {{Modeling and Adaptive Filtering for Systems with Output Nonlinearity}},
  booktitle = {AES Convention 140},
  note = {Paper 9564},
  year = {2016},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18262}
}

TY  - CPAPER
TI  - Modeling and Adaptive Filtering for Systems with Output Nonlinearity
AU  - Soltanmohammadi, Erfan
AU  - Painter, Christopher
AU  - Jain, Kapil
T2  - AES Convention 140
M1  - Paper 9564
PY  - 2016
DA  - 2016/05/06
UR  - https://aes.org/publications/elibrary-page/?id=18262
PB  - Audio Engineering Society
LA  - en
AB  - Many practical systems are nonlinear in nature, and the Volterra series, also known as nonlinear convolution, is widely used to model these systems. For nonlinear systems with infinite memory, such a modeling approach is usually not feasible because of multiple infinite summations. In practice, the full Volterra series representation of such a system is either approximated by just a few terms, or is otherwise simplified. In an audio system, a useful approximation is to model all memoryless and dynamical nonlinear effects as a combined nonlinearity at its output. In this paper we propose a new Volterra-based structure that accommodates nonlinear systems with output nonlinearity and infinite memory. We then propose an adaptation approach to estimate the Volterra kernels based on the Least Mean Squares (LMS) approach.
ER  -