Nonlinear acoustic echo cancellation using low-complexity low-rank recursive least-squares algorithms

Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained u...

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Vydané v:	Signal processing Ročník 225; s. 109623
Hlavní autori:	Patel, Vinal, Bhattacharjee, Sankha Subhra, Jensen, Jesper Rindom, Christensen, Mads Græsbøll, Benesty, Jacob
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.12.2024
Predmet:	Linear-in-the parameter nonlinear filter Low-rank approximation Nearest Kronecker product Nonlinear echo cancellation Recursive least-squares algorithm Nonlinear echo cancellation Nearest Kronecker product Linear-in-the parameter nonlinear filter Low-rank approximation Recursive least-squares algorithm
ISSN:	0165-1684
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Popis
Shrnutí:	Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained using recursive least-squares becomes an attractive choice to achieve faster convergence. Moreover, huge computational burden of RLS makes it unsuitable in practical applications like echo cancellation. While low complexity versions of RLS are widely available in literature, they still suffer from low convergence speed. To address this issue, we propose a nonlinear acoustic echo cancellation (NAEC) system using AEFLN-RLS, based on the nearest Kronecker product (NKP) decomposition and low-rank approximation technique, which not only reduces computational complexity but also achieves improved convergence speed (especially tracking). To further improve the echo cancellation performance in non-stationary conditions, a variable regularization approach based NKP-AEFLN-RLS system is also proposed. To also reduce the computational complexity further, dichotomous coordinate descent (DCD) updates are incorporated into the proposed NKP-AEFLN-RLS NAEC system and its variable regularization version. Experimental results show the effectiveness of the proposed algorithms, with the variable regularized version of the algorithm using DCD iterations showing the best compromise between convergence capability and lower computational complexity. •A NKPD based adaptive exponential functional link structure updated using the RLS is developed.•Near optimal VR scheme VR-NKP-AEFLN-RLS which updates the regularization parameter at each step.•Reduced computation complexity variant of proposed algorithms using a DCD approach is developed.•A detailed comparison of the computational complexity of proposed algorithms is also presented.
ISSN:	0165-1684
DOI:	10.1016/j.sigpro.2024.109623