scholarly journals A New Variable Step-Size NLMS Algorithm and its Performance Evaluation in Echo Cancelling Applications

2011 ◽  
Vol 9 (03) ◽  
Author(s):  
F. M. Casco-Sánchez ◽  
R. C. Medina-Ramírez ◽  
M. López-Guerrero
Keyword(s):  
Cross Correlation ◽  
Echo Cancellation ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filter Output ◽  
Nlms Algorithm ◽  
Variable Step ◽  
Size Adjustment ◽  
Normalized Lms

In this work we introduce a variable step-size normalized LMS algorithm for adaptive echo cancellation in a FIRstructure. In the proposed scheme, the step-size adjustment is controlled by using the square of the cross-correlationbetween the squared output error and the adaptive filter output. The proposed algorithm (that we call VSSSC aftervariable step size based on the squared cross-correlation) was evaluated using white noise and speech signals.Simulation results show that our proposal achieves better performance than similar algorithms in single and doubletalk. The proposed algorithm can be used in a number of applications such as in echo reduction for long-haul voicecommunications.

A Variable Step Size LMS Based on Sparsity for System Identification

2013 ◽  
Vol 475-476 ◽  
pp. 1060-1066
Author(s):  
X.Q. Chen ◽  
Hua Ju ◽  
Wei Fan ◽  
W.G. Huang ◽  
Z.K. Zhu
Keyword(s):  
Steady State ◽  
System Identification ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Practical Applications ◽  
Variable Step ◽  
Size Adjustment ◽  
State Error

In many practical applications, the impulse responses of the unknown system are sparse. However, the standard Least Mean Square (LMS) algorithm does not make full use of the sparsity, and the general sparse LMS algorithms increase steady-state error because of giving much large attraction to the small factor. In order to improve the performance of sparse system identification, we propose a new algorithm which introduces a variable step size method into the Reweighted Zero-Attracting LMS (RZALMS) algorithm. The improved algorithm, whose step size adjustment is controlled by the instantaneous error, is called Variable step size RZALMS (V-RZALMS). The variable step size leads to yielding smaller steady-state error on the premise of higher convergent speed. Moreover, the sparser the system is, the better the V-RZALMS performs. Three different experiments are implemented to validate the effectiveness of our new algorithm.

Simulation Realization of Adaptive Filtering Based on a Normalized LMS Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1159-1163
Author(s):  
Ya Feng Li ◽  
Zi Wei Zheng
Keyword(s):  
Adaptive Filtering ◽  
Notch Filter ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Adaptive Notch Filter ◽  
Variable Step ◽  
Simulation Results ◽  
Normalized Lms

This paper presents the new algorithm which is an improved normalized variable step size LMS adaptive filtering algorithm. A normalized LMS algorithm with variable step size iterative formula is deduced and at the same time the simulation results prove that the new algorithm has good performance. The LMS adaptive filtering algorithm has been widely used in many applications such as system identification, noise cancellation and the adaptive notch filter ,the paper analyses the application and implement the simulation by matlab. the result shows the proposed algorithm has been applied well.

A variable‐step size NLMS algorithm based on the cross‐correlation between the squared output error and the near‐end input signal

2019 ◽  
Vol 14 (8) ◽  
pp. 1197-1202
Author(s):  
Fausto Casco‐Sanchez ◽  
Miguel Lopez‐Guerrero ◽  
Sergio Javier‐Alvarez ◽  
Reyna Carolina Medina‐Ramirez
Keyword(s):  
Input Signal ◽  
Cross Correlation ◽  
Variable Step Size ◽  
Step Size ◽  
Output Error ◽  
Nlms Algorithm ◽  
Variable Step ◽  
The Cross
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