Analysis and design of a signed regressor LMS algorithm for stationary and nonstationary adaptive filtering with correlated Gaussian data

Eweda, E.

doi:10.1109/31.62411

Cited by 100 publications

(55 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is customary to introduce simplifying assumptions that tend to lead to the reasonable agreements between theory and practice [1][2][3]. Many contributions focus on a particular algorithm, making more or less restrictive assumptions on the input signal [4][5][6][7][8]. Obviously, a more general analysis encompassing as many different algorithms as possible as special cases, while at the same time making as few restrictive assumptions as possible, is highly desirable [9,10].…”

Section: Introductionmentioning

confidence: 99%

On the application of a unified adaptive filter theory in the performance prediction of adaptive filter algorithms

Abadi

Husøy

2009

Digital Signal Processing

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

On the application of a unified adaptive filter theory in the performance prediction of adaptive filter algorithms

Abadi

Husøy

2009

Digital Signal Processing

View full text Add to dashboard Cite

“…The correction applied to the weight vector w(n) at iteration n+1 is "normalized" with respect to the squared Euclidian norm of the input vector x(n) at iteration n. We may view the NLMS algorithm as a timevarying step-size algorithm, calculating the convergence factor μ as in Eq. (4) [7].…”

Section: Nlms Algorithmmentioning

confidence: 99%

Implementation of Adaptive Algorithm for PCG Signal Denoising

Potdar¹,

Meshram²,

Dewangan³

et al. 2015

International Journal of Innovative Research in Electrical, Ele

View full text Add to dashboard Cite

show abstract

“…Where ( ) is a regularized step size with 0 < <2.Substituting Qin the SRLMS [27][28] weight vector fill in equation with Q(d) directs to the NLMS, which is known as …”

Section: Sign Regressor Lms (Srlms) Techniquementioning

confidence: 99%

Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications

Jyoshna¹,

Rahman²

2018

IJET

View full text Add to dashboard Cite

Removal of noise components of speech signals in mobile applications is an important step to facilitate high resolution signals to the user. Throughout the communication method the speech signals are tainted by numerous non stationary noises. The Least Mean Square (LMS) technique is a fundamental adaptive technique usedbroadly in numerouspurposes as anoutcome of its plainness as well as toughness. In LMS technique, an importantfactor is the step size. It bewell-known that if the union rate of the LMS technique will be rapidif the step size is speedy, but the steady-state mean square error (MSE) will raise. On the rival, for the diminutive step size, the steady state MSE will be minute, but the union rate will be conscious. Thus, the step size offers anexchange among the convergence rate and the steady-state MSE of the LMS technique. Build the step size variable before fixed to recover the act of the LMS technique, explicitly, prefer large step size values at the time of the earlyunion of the LMS technique, and usetiny step size values when the structure is near up to its steady state, which results in Normalized LMS (NLMS) algorithms. In this practice the step size is not stable and changes along with the fault signal at that time. The Less mathematical difficulty of the adaptive filter is extremely attractive in speech enhancement purposes. This drop usually accessible by extract either the input data or evaluation fault. The algorithms depend on an extract of fault or data are Sign Regressor (SR) Algorithms. We merge these sign version to various adaptive noise cancellers. SR Weight NLMS (SRWN-LMS), SR Error NLMS (SRENLMS), SR Unbiased LMS (SRUBLMS) algorithms are individual introduced as a quality factor. These Adaptive noise cancellers are compared with esteem to Signal to Noise Ratio Improvement (SNRI).

show abstract

Analysis and design of a signed regressor LMS algorithm for stationary and nonstationary adaptive filtering with correlated Gaussian data

Cited by 100 publications

References 17 publications

On the application of a unified adaptive filter theory in the performance prediction of adaptive filter algorithms

On the application of a unified adaptive filter theory in the performance prediction of adaptive filter algorithms

Implementation of Adaptive Algorithm for PCG Signal Denoising

Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications

Contact Info

Product

Resources

About