Application of Fast BLMS Algorithm in Acoustic Echo Cancellation

Yue, Zhao; Qiang, Li Nian

doi:10.1016/j.egypro.2011.11.312

Cited by 1 publication

(2 citation statements)

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“…The basic principle of the BLMS is that the adaptive filter coefficient remains the same in the cycle M, and the coefficient would be updated using the LMS algorithm when the Mth point in a cycle arrives [5][6]. NBLMS is updating the coefficient with a variable step size instead of the fixed step size of the BLMS when the Mth point arrives.…”

Section:  Vs_blmsmentioning

confidence: 99%

“…According to the Sigmoid function, the reference [3] creates the nonlinear correlation between the estimated error and the variable step size. The reference [4] introduces the correction coefficient  and the forgetting factor i  . In order to update the variable step size and adjust the tap coefficient, it calculates the deviation degree L  through the nonlinear weight of the current and past L estimation errors .…”

Section: Introductionmentioning

confidence: 99%

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The research of λVS_BLMS algorithm in digital hearing aid

Fan

Xian-hui

Zhang

et al. 2015

2015 4th International Conference on Computer Science and Network Technology (ICCSNT)

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The Least Mean Square (LMS)algorithm is the most basic adaptive echo cancellation algorithm. However, the fixed step size of LMS does not solve the contradiction between the convergence rate and the steady state error which are all used for judging the performance of algorithm. This paper references the Sigmoid function and creates the nonlinear relation between estimated error ( ) e n and variable step size ( ) step n . Furthermore, the paper also introduces the error coefficient L  and deviation degree L  to modify the step size ( ) step n , which references the "forgetting factor" in the RLS algorithm. In order to satisfy the requirements about the real-time and the low power consumption in digital hearing aid, the paper also integrates into the Blocked LMS (BLMS) algorithm and proposes the new algorithm λVS_BLMS. The simulation verifies that the λVS_BLMS posses the better performances and the smaller computation.

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Section:  Vs_blmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%