Paper Info
Title
M-Estimate Based Normalized Subband Adaptive Filter Algorithm: Performance Analysis and Improvements
Abstract
This article studies the mean and mean-square behaviors of the M-estimate based normalized subband adaptive filter algorithm (M-NSAF) with robustness against impulsive noise. Based on the contaminated-Gaussian noise model, the stability condition, transient and steady-state results of the algorithm are formulated analytically. These analysis results help us to better understand the M-NSAF performance in impulsive noise. To further obtain fast convergence and low steady-state estimation error, we derive a variable step size (VSS) M-NSAF algorithm. This VSS scheme is also generalized to the proportionate M-NSAF variant for sparse systems. Computer simulations on the system identification in impulsive noise and the acoustic echo cancellation with double-talk are performed to demonstrate our theoretical analysis and the effectiveness of the proposed algorithms.
Year
DOI
Venue
2020
10.1109/TASLP.2019.2950597
IEEE/ACM Transactions on Audio, Speech, and Language Processing
Keywords
DocType
Volume
Convergence,Echo cancellers,Steady-state,Estimation error,Speech processing,Adaptive systems
Journal
28
Issue
ISSN
Citations 
1
2329-9290
3
PageRank 
References 
Authors
0.38
22
6
Name
Order
Citations
PageRank
Yi Yu119417.90
Hongsen He2234.89
Badong Chen391965.71
Jianghui Li452.45
Youwen Zhang550.75
Lu Lu610120.10