Using prewhitening and stepsize control to improve the performance of the LMS algorithm for acoustic echo compensation

Frenzel, R.; Hennecke, M.E.

doi:10.1109/iscas.1992.230432

Cited by 18 publications

(4 citation statements)

References 2 publications

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“…The log LRT for (4) accepts hypothesis when (6) exceeds an appropriate threshold [12, p. 34]. Since the last term in this expression is not a function of the observables, the log LRT simplifies to (7) where is a threshold setting determined by and .…”

Section: B Log-likelihood Ratio Testmentioning

confidence: 99%

Echo Cancellation—A Likelihood Ratio Test for Double-Talk Versus Channel Change

Bershad

Tourneret

2006

IEEE Trans. Signal Process.

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Echo cancellers (ECs) are in wide use in both electrical (four-wire to two-wire mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The control logic can be quite complicated since it is often not easy to discriminate between the echo signal and the near-end speaker. This paper derives a log-likelihood ratio test (LRT) for deciding between double-talk (freeze weights) and a channel change (adapt quickly) using a stationary Gaussian stochastic input signal model. The probability density function (pdf) of a sufficient statistic under each hypothesis is obtained, and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change based upon a single look. However, postdetection integration of approximately 100 sufficient statistic samples yields a detection probability close to unity (0.99) with a small false-alarm probability (0.01).

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Section: B Log-likelihood Ratio Testmentioning

confidence: 99%

Echo Cancellation—A Likelihood Ratio Test for Double-Talk Versus Channel Change

Bershad

Tourneret

2006

IEEE Trans. Signal Process.

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“…However, the convergence rate of these algorithms will degrade in the presence of colored input signals such as speech [1]. Several solutions have been proposed to ameliorate the performance, which often introduce the decorrelation filters to whiten the input signals [2] [3] [4]. Yet, this filtered structure needs to compute and update the filter coefficients at every sample period, which in deed increases the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Integrating Acoustic Echo Cancellation In LPC-based Speech Coders

Lin

et al. 2006

2006 International Symposium on Communications and Information Technologies

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This paper presents a novel acoustic echo canceller (AEC) consisting of an adaptive FIR filter and a double-talk detector. By employing the widely used LPC-based speech coder for decorrelation, we develop a variable step-size decorrelation NLMS algorithm for the adaptive FIR filter and an echo-path mismatch detection algorithm for the double-talk detector. This new approach reduces the computational complexity from O(N2) to O(N) for both algorithms of echo cancellation and doubletalk detection. Simulation results show that the proposed algorithms improve the performance of both echo cancellation and doubletalk detection greatly compared to other algorithms.

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“…The resulting ( ) h n ξ can be regarded as an equalizing filter with the autocorrelation sequence of its impulse response given by [29], [39], [40], but in our case, the decorrelation filter is embedded in the algorithm forming a self-designing decorrelation mechanism.…”

Section: Weighted Criterionmentioning

confidence: 99%

Subband adaptive algorithms for high-order transversal filters

Lee¹

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Adaptive filters have been employed in wide range of applications where signal characteristics are not available, or are time-varying, such as acoustic echo cancellation and adaptive noise cancellation. Despite their widespread use, performance of the adaptive filters is degraded in some applications by two major factors, namely, (i) coloring of the input signal, and (ii) long impulse response of the unknown system. The classical least-mean-square (LMS) algorithm is computationally efficient but its convergence speed deteriorates as the eigenvalue spread of the input autocorrelation matrix increases due to the coloring of the input signal. The convergence problem worsens when the order of the adaptive filter is increased in order to model the unknown system with sufficient accuracy. In contrast, recursive least-squares (RLS) algorithm is adequate in dealing with colored excitation with large spectral dynamic range. Nevertheless, high computational complexity renders the algorithm not applicable in real-time for the case of high-order adaptive filter. Those problems have motivated various approaches of employing subband and multirate techniques in designing computationally efficient adaptive algorithms with improved convergence performance against high eigenvalue disparity. Conventional subband adaptive filters (SAFs) decompose the input signal and desired response into multiple contiguous spectral bands, decimate the subband signals, process each subband with separate adaptive subfilters, and finally interpolate and recombine the resulting subband signals to obtain a filtered output. Intuitively, faster convergence is possible because the spectral dynamic range is greatly reduced in each subband. Furthermore, the computational burden can be reduced by decimating both the order and adaptation rate of the subfilters. Yet, detailed analysis shows that the convergence ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library viii rate of the conventional SAF is limited by aliasing and band-edge effects, in spite of a number of modifications that have been proposed, such as, introducing adaptive cross-filters or spectral gaps between adjacent subfilters, as well as oversampled scheme. This thesis describes, analyzes, and generalizes a new class of SAFs, called the normalized SAF (NSAF), whereby the adaptive filter is no longer separated into subfilters. Instead, subband signals, which are normalized by their respective subband input variance, are used to adapt the fullband tap weights of a modeling filter. The

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Using prewhitening and stepsize control to improve the performance of the LMS algorithm for acoustic echo compensation

Cited by 18 publications

References 2 publications

Echo Cancellation—A Likelihood Ratio Test for Double-Talk Versus Channel Change

Echo Cancellation—A Likelihood Ratio Test for Double-Talk Versus Channel Change

Integrating Acoustic Echo Cancellation In LPC-based Speech Coders

Subband adaptive algorithms for high-order transversal filters

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