A joint-optimization NSAF algorithm based on the first-order Markov model
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.
Cite
@article{arxiv.1609.04108,
title = {A joint-optimization NSAF algorithm based on the first-order Markov model},
author = {Yi Yu and Haiquan Zhao},
journal= {arXiv preprint arXiv:1609.04108},
year = {2017}
}
Comments
8 pages, 4 figures, accepted by Signal, Image and Video Processing on 18-Sep-2016