Set-membership improved normalized subband adaptive filter algorithms for acoustic echo cancellation

TL;DR

This paper introduces set-membership versions of INSAF algorithms for acoustic echo cancellation, achieving lower steady-state error and reduced computational complexity, with enhanced performance through smooth variants.

Contribution

It presents novel set-membership adaptations of INSAF algorithms, improving noise robustness and efficiency in acoustic echo cancellation tasks.

Findings

Smaller steady-state error achieved

Significant reduction in computational complexity

Superiority demonstrated in acoustic echo cancellation simulations

Abstract

In order to improve the performances of recently-presented improved normalized subband adaptive filter (INSAF) and proportionate INSAF algorithms for highly noisy system, this paper proposes their set-membership versions by exploiting the theory of set-membership filtering. Apart from obtaining smaller steady-state error, the proposed algorithms significantly reduce the overall computational complexity. In addition, to further improve the steady-state performance for the algorithms, their smooth variants are developed by using the smoothed absolute subband output errors to update the step sizes. Simulation results in the context of acoustic echo cancellation have demonstrated the superiority of the proposed algorithms.