A joint-optimization NSAF algorithm based on the first-order Markov model

TL;DR

This paper introduces a joint-optimization NSAF algorithm based on the first-order Markov model, improving convergence speed and steady-state error in adaptive filtering, especially for acoustic echo cancellation.

Contribution

It provides a convergence analysis of the NSAF and proposes a joint-optimization method for step size and regularization parameter based on the Markov model.

Findings

Achieves a good tradeoff between convergence speed and steady-state error.

Demonstrates superior performance in acoustic echo cancellation simulations.

Abstract

Recently, the normalized subband adaptive filter (NSAF) algorithm has attracted much attention for handling the colored input signals. Based on the first-order Markov model of the optimal tap-weight vector, this paper provides a convergence analysis of the standard NSAF. Following the analysis, both the step size and the regularization parameter in the NSAF are jointly optimized in such a way that minimizes the mean square deviation. The resulting joint-optimization step size and regularization parameter (JOSR-NSAF) algorithm achieves a good tradeoff between fast convergence rate and low steady-state error. Simulation results in the context of acoustic echo cancellation demonstrate good features of the proposed algorithm.