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Tuned feedforward LMS filter with feedback control

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TLDR
In this article, the authors proposed a method to automatically and adaptively tune a leaky, normalized least-mean-square (LNLMS) algorithm so as to maximize the stability and noise reduction performance in feedforward adaptive noise cancellation systems.
Abstract
A method to automatically and adaptively tune a leaky, normalized least-mean-square (LNLMS) algorithm (24) so as to maximize the stability and noise reduction performance in feedforward adaptive noise cancellation systems. The automatic tuning method provides for time-varying tuning parameters λk and µk that are functions of the instantaneous measured acoustic noise signal (12), weight vector length, and measurement noise variance. The method addresses situations in which signal-to-noise ratio varies substantially due to nonstationary noise fields, affecting stability, convergence, and steady-state noise cancellation performance of LMS algorithms. The method has been embodied in the particular context of active noise cancellation in communication headsets. However, the method is generic, in that it is applicable to a wide range of systems subject to nonstationary, i.e., time-varying, noise fields, including sonar, radar, echo cancellation, and telephony.

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References
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The tap-leakage algorithm: An algorithm for the stable operation of a digitally implemented, fractionally spaced adaptive equalizer

TL;DR: In this article, a simple modification in the tap-adjustment algorithm was proposed to prevent the buildup of large coefficient values by systematically leaking or decreasing the magnitudes of all the equalizer tap weights.
Journal ArticleDOI

Leaky LMS algorithm: MSE analysis for Gaussian data

TL;DR: An analytical treatment of the mean-square error (MSE) performance for the leaky LMS adaptive algorithm for Gaussian input data and exact expressions that completely characterize the second moment of the coefficient vector and algorithm steady-state excess MSE are developed.
Patent

Active noise reduction for audiometry

TL;DR: In this article, active noise reduction (ANR) is incorporated into audiometry testing in a variety of formats, including analog feedback, digital feedback, adaptive feedforward, and adaptive feedback noise control schemes.
Journal ArticleDOI

The stability of variable step-size LMS algorithms

TL;DR: The approach taken here is a generalization of the classical method of analyzing, the exponential stability of the weight covariance equation for LMS, which can be upper bounded by the solution of a linear time-invariant difference equation, after appropriately dealing with certain nonlinear terms.
Patent

Method for tuning an adaptive leaky LMS filter

TL;DR: In this article, a method to automatically and adaptively tune a leaky, normalized least-mean-square (LNLMS) algorithm so as to maximize the stability and noise reduction performance in feedforward adaptive noise cancellation systems was proposed.