Variable Step Size Algorithm for Blind Source Separation Using a Combination of Two Adaptive Separation Systems

2009 ◽  
Author(s):  
Ou Shifeng ◽  
Gao Ying ◽  
Jin Gang ◽  
Zhang Xuehui
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Variable Step Size ◽  
Step Size ◽  
Separation Systems ◽  
Variable Step

Variable step-size EASI algorithm for sequential blind source separation

2004 ◽  
Vol 40 (6) ◽  
pp. 393 ◽  
Author(s):  
J.A. Chambers ◽  
M.G. Jafari ◽  
S. McLaughlin
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step

scholarly journals Variable Step-Size Method Based on a Reference Separation System for Source Separation

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Pengcheng Xu ◽  
Zhigang Yuan ◽  
Wei Jian ◽  
Wei Zhao
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Variable Step Size ◽  
Separation System ◽  
Step Size ◽  
Original Source ◽  
Variable Step ◽  
Initial Setting ◽  
Free Case ◽  
Source Signals

Traditional variable step-size methods are effective to solve the problem of choosing step-size in adaptive blind source separation process. But the initial setting of learning rate is vital, and the convergence speed is still low. This paper proposes a novel variable step-size method based on reference separation system for online blind source separation. The correlation between the estimated source signals and original source signals increases along with iteration. Therefore, we introduce a reference separation system to approximately estimate the correlation in terms of mean square error (MSE), which is utilized to update the step-size. The use of “minibatches” for the computation of MSE can reduce the complexity of the algorithm to some extent. Moreover, simulations demonstrate that the proposed method exhibits superior convergence and better steady-state performance over the fixed step-size method in the noise-free case, while converging faster than classical variable step-size methods in both stationary and nonstationary environments.

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