scholarly journals Hum Noise Reduction using Novel Complementary Pair VSSLMS Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1720-1727
Keyword(s):  
Fourier Transform ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Short Time Fourier Transform ◽  
Complementary Pair ◽  
Step Size ◽  
Active Noise ◽  
Variable Step ◽  
Least Mean Square Algorithm ◽  
Short Time

This paper presents a novel Complementary Pair Variable Step-size (CPVSS) Least Mean Square Algorithm which is implemented for active noise cancellation application. The result presents the effect of filtration to remove the 50 Hz ac hum noise from the 500 Hz audio tone. Discrete Fourier Transform, Short Time Fourier Transform, and Welch Periodogram of the noisy signal and filtered signal have been presented which shows effective improvement in SNR. The simulation study has shown the superiority of this algorithm over other algorithms under consideration.

A novel variable step size least mean square method for adaptive micro-vibration control

2021 ◽  
pp. 107754632110228
Author(s):  
Yubin Fang ◽  
Xiaojin Zhu ◽  
Xiaobing Zhang
Keyword(s):  
Variable Step Size ◽  
Mean Square ◽  
Least Mean Square ◽  
Forgetting Factor ◽  
Step Size ◽  
Factor Method ◽  
Variable Step ◽  
Least Mean Square Algorithm ◽  
Variable Forgetting Factor ◽  
Micro Vibration

The variable step size least mean square algorithm has been suggested since a number of years as a potential solution for improving the performance of least mean square algorithm. In this article, the variable step size least mean square algorithm is classified by the techniques which are used to update step size. Unfortunately, for variable step size least mean square algorithms with forgetting factor, a constant forgetting factor may slow down its convergence speed. For this reason, a variable forgetting factor method for variable step size least mean square is proposed in this article. First, the convergence analysis of a new variable step size least mean square algorithm with the variable forgetting factor is provided. Then, simulations expose the characteristics of this variable forgetting factor method. Last, a micro-vibration control experimental system is established. Four typical variable step size least mean square algorithms and their variable forgetting factor modified version are verified through experiments. The results show that the proposed variable forgetting factor method can effectively improve convergence speed while maintaining the steady-state performance of the variable step size least mean square algorithm with the constant forgetting factor.

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