General Robust Subband Adaptive Filtering: Algorithms and Applications

被引：12
作者：
Yu, Yi [1 ]
He, Hongsen [1 ]
de Lamare, Rodrigo C. [2 ]
Chen, Badong [3 ]
机构：
[1] Southwest Univ Sci & Technol, Sch Informat Engn, Robot Technol Used Special Environm Key Lab Sichu, Mianyang 621010, Sichuan, Peoples R China
[2] Pontificia Univ Catolica Rio de Janeiro, CETUC, BR-22451900 Rio de Janeiro, Brazil
[3] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Shaanxi, Peoples R China
来源：
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING | 2022年 / 30卷
基金：
中国国家自然科学基金;
关键词：
Convergence; Steady-state; Computational complexity; Adaptive systems; Uncertainty; Speech processing; Robustness; Echo cancellation; impulsive noise; subband adaptive filter; variable regularization parameter; VARIABLE STEP-SIZE; IMPULSIVE NOISE; PERFORMANCE ANALYSIS; ERROR ANALYSIS; REGULARIZATION; CONVERGENCE; CORRENTROPY; SCALER; MATRIX;
D O I：
10.1109/TASLP.2022.3183935
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF can be reduced to a variable regularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF outperforms its counterparts.
引用
收藏
页码：2128 / 2140
页数：13
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