Adaptive filters are used in the situation where the filter coefficients have to be changed simultaneously according to the requirement. Adaptive filters are needed for fast convergence rate and low mean square error. Many algorithms have been proposed and proved that they have better convergence speed and tracking abilities. This paper shows the ability of adaptive filter for noise cancellatio...

This paper investigates the application of the delayed normalized least mean square (DNLMS) algorithm to echo cancellation. In order to reduce the amount of computations, DNLMS is modified by using computationallyefficient techniques including the M-Max algorithm, a Stopand-go (SAG) algorithm, and Power-of-two (POT) quantization. For the SAG algorithm, a new stopping criterion related to the re...

In this work, a recently derived recursive least-square (RLS) algorithm to train multi layer perceptron (MLP) is used in an MLP-based decision feedback equalizer (DFE) instead of the back propagation (BP) algorithm. Its performance is investigated and compared to those of MLP-DFE based on the BP algorithm and the simple DFE based on the least-mean square (LMS) algorithm. The results show improv...

We present a general block formulation of the least mean square (LMS) algorithm for adaptive filtering. This formulation has an exact equivalence with the original LMS algorithm, hence retaining the same convergence properties, while allowing a reduction in arithmetic complexity, even for very small block lengths. Working with small block lengths is very interesting from an implementation point...

This paper presents a comparative performance study between the recently proposed time-varying LMS (TVLMS) algorithm and other two main adaptive approaches: the least-mean square (LMS) algorithm and the recursive leastsquares (RLS) algorithm. Three performance criteria are utilized in this study: the algorithm execution time, the minimum meansquared error (MSE), and the required filter order. T...

New steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth, etc., sense. During adaptation, the weights undergo exponential relaxation toward their optimal solutions. T ime constants have been derived, and surprisingly they turn out to be proportional to the time constants that would have been obtained if the ste...

Ahstpact-Convergence properties of a continuously adaptive digital lattice filter. used as a linear predictor are investigated for both an unnormalized and a normalized gradient adaptation algorithm. The PARCOR coefficient mean values and the output mean-square error (MSE) are approximated and a simple model is described which approximates these quantities as functions of time. Calculated curve...

Since both the least mean-square (LMS) and least mean-fourth (LMF) algorithms suffer individually from the problem of eigenvalue spread, so will the mixed-norm LMS-LMF algorithm. Therefore, to overcome this problem for the mixed-norm LMS-LMF, we are adopting here the same technique of normalization (normalizing with the power of the input) that was successfully used with the LMS and LMF separat...

Perceptual acoustic echo cancellers were developed by mainly considering human hearing thresholds of different acoustic frequencies in the past. In addition to the different frequency sensitivities, the human brain further analyzes sounds in terms of their spectral and temporal modulations. In this paper, we extend the perceptual normalized least mean square (P-NLMS) algorithm by adding a secon...