A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

TL;DR

This paper introduces a new class of efficient nonlinear adaptive filters designed for online nonlinear modeling, combining low-complexity expansions and frequency-domain adaptation to balance performance and computational resources.

Contribution

The paper proposes a novel class of functional link adaptive filters with low-complexity expansions and frequency-domain adaptation, optimized for online nonlinear modeling tasks.

Findings

Effective in acoustic echo cancellation

Achieves good performance with limited computational resources

Outperforms existing methods in nonlinear conditions

Abstract

Nonlinear models are known to provide excellent performance in real-world applications that often operate in non-ideal conditions. However, such applications often require online processing to be performed with limited computational resources. To address this problem, we propose a new class of efficient nonlinear models for online applications. The proposed algorithms are based on linear-in-the-parameters (LIP) nonlinear filters using functional link expansions. In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters. Among this family of algorithms, we also define the partitioned-block frequency-domain FLAF, whose implementation is particularly suitable for online nonlinear modeling problems. We assess and compare frequency-domain FLAFs with different expansions providing the best possible tradeoff between performance and computational complexity. Experimental results prove that the proposed algorithms can be considered as an efficient and effective solution for online applications, such as the acoustic echo cancellation, even in the presence of adverse nonlinear conditions and with limited availability of computational resources.