Discrete-time variance tracking with application to speech processing

Two new discrete-time algorithms are presented for tracking variance and reciprocal variance. The closed loop nature of the solutions to these problems makes this approach highly accurate and can be used recursively in real time. Since the Least-Mean Squares (LMS) method of parameter estimation requires an estimate of variance to compute the step size, this technique is well suited to applications such as speech processing and adaptive filtering

An affine combination of two LMS adaptive filters - Transient mean-square analysis

This paper studies the statistical behavior of an affine combination of the outputs of two LMS adaptive filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor $\lambda(n)$ is restricted to the interval $(0,1)$. The viewpoint is taken that each of the two filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the MSE. First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSD's of either filter

A Robust Variable Step Size Fractional Least Mean Square (RVSS-FLMS) Algorithm

In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).Comment: 15 pages, 3 figures, 13th IEEE Colloquium on Signal Processing & its Applications (CSPA 2017

On feed-through terms in the lms algorithm

The well known least mean squares (LMS) algorithm is studied as a control system. When applied in a noise canceller a block diagram approach is used to show that the step size has two upper limits. One is the conventional limit beyond which instability results. The second limit shows that if the step size is chosen to be too large then feed-through terms consisting of signal times noise will result in an additive term at the noise canceller output. This second limit is smaller than the first and will cause distortion at the noise canceller output