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38
A nonparametric VSS NLMS algorithm
 IEEE Signal Process. Lett
, 2006
"... Abstract—The aim of a variable step size normalized leastmeansquare (VSSNLMS) algorithm is to try to solve the conflicting requirement of fast convergence and low misadjustment of the NLMS algorithm. Numerous VSSNLMS algorithms can be found in the literature with a common point for most of them ..."
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Cited by 28 (12 self)
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Abstract—The aim of a variable step size normalized leastmeansquare (VSSNLMS) algorithm is to try to solve the conflicting requirement of fast convergence and low misadjustment of the NLMS algorithm. Numerous VSSNLMS algorithms can be found in the literature with a common point for most of them: they may not work very reliably since they depend on several parameters that are not simple to tune in practice. The objective of this letter is twofold. First, we explain a simple and elegant way to derive VSSNLMStype algorithms. Second, a new nonparametric VSSNLMS is proposed that is easy to control and gives good performances in the context of acoustic echo cancellation. Index Terms—Acoustic echo cancellation, adaptive filters, least mean square (LMS), normalized LMS (NLMS), variable step size NLMS. I.
A normalized adaptation scheme for the convex combination of two adaptive filters
 in Proc. ICASSP’08, Las Vegas, NV
"... Adaptive filtering schemes are subject to different tradeoffs regarding their steadystate misadjustment, speed of convergence and tracking performance. To alleviate these compromises, a new approach has recently been proposed, in which two filters with complementary capabilities adaptively mix thei ..."
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Cited by 17 (14 self)
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Adaptive filtering schemes are subject to different tradeoffs regarding their steadystate misadjustment, speed of convergence and tracking performance. To alleviate these compromises, a new approach has recently been proposed, in which two filters with complementary capabilities adaptively mix their outputs to get an overall filter of improved performance. Following this approach, in this paper we propose a new normalized rule for adapting the mixing parameter that controls the combination. The new update rule preserves the good features of the standard scheme and is more robust to changes in the filtering scenario, for instance when the signal to noise ratio (SNR) is time varying. The benefits of the normalized scheme are illustrated analytically and with a number of experiments in both stationary and tracking situations. Index Terms — Adaptive filters, least mean square methods, tracking filters 1.
BVariable stepsize NLMS algorithm for undermodeling acoustic echo cancellation
 IEEE Signal Process. Lett
, 2008
"... Abstract—In acoustic echo cancellation (AEC) applications, where the acoustic echo paths are extremely long, the adaptive filter works most likely in an undermodeling situation. Most of the adaptive algorithms for AEC were derived assuming an exact modeling scenario, so that they do not take into a ..."
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Cited by 11 (3 self)
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Abstract—In acoustic echo cancellation (AEC) applications, where the acoustic echo paths are extremely long, the adaptive filter works most likely in an undermodeling situation. Most of the adaptive algorithms for AEC were derived assuming an exact modeling scenario, so that they do not take into account the undermodeling noise. In this letter, a variable stepsize normalized leastmeansquare (VSSNLMS) algorithm suitable for the undermodeling case is proposed. This algorithm does not require any a priori information about the acoustic environment; as a result, it is very robust and easy to control in practice. The simulation results indicate the good performance of the proposed algorithm. Index Terms—Acoustic echo cancellation, adaptive filters, normalized least mean square (NLMS), undermodeling system identification, variable stepsize NLMS. I.
Optimum variable explicit regularized affine projection algorithm
 in Proc. ICASSP06
, 2006
"... Abstract—A variable regularized affine projection algorithm (VRAPA) is introduced, without requiring the classical step size. Its use is supported from different points of view. First, it has the property of being H1 optimal and it satisfies certain error energy bounds. Second, the timevarying reg ..."
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Cited by 8 (5 self)
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Abstract—A variable regularized affine projection algorithm (VRAPA) is introduced, without requiring the classical step size. Its use is supported from different points of view. First, it has the property of being H1 optimal and it satisfies certain error energy bounds. Second, the timevarying regularization parameter is obtained by maximizing the speed of convergence of the algorithm. Although we first derive the VRAPA for a linear time invariant (LTI) system, we show that the same expression holds if we consider a timevarying system following a firstorder Markov model. We also find expressions for the power of the steadystate error vector for the VRAPA and the standard APA with no regularization parameter. Particularly, we obtain quite different results with and without using the independence assumption between the a priori error vector and the measurement noise vector. Simulation results are presented to test the performance of the proposed algorithm and to compare it with other schemes under different situations. An important conclusion is that the former independence assumption can lead to very inaccurate steadystate results, especially when high values of the projection order are used. Index Terms—Adaptive filtering, affine projection algorithm (APA), filtering, regularization, steadystate analysis. I.
A variable stepsize affine projection algorithm designed for acoustic echo cancellation
 IEEE Trans. Audio, Speech, Lang. Process
, 2008
"... Abstract—The adaptive algorithms used for acoustic echo cancellation (AEC) have to provide 1) high convergence rates and good tracking capabilities, since the acoustic environments imply very long and timevariant echo paths, and 2) low misadjustment and robustness against background noise variatio ..."
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Cited by 6 (2 self)
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Abstract—The adaptive algorithms used for acoustic echo cancellation (AEC) have to provide 1) high convergence rates and good tracking capabilities, since the acoustic environments imply very long and timevariant echo paths, and 2) low misadjustment and robustness against background noise variations and doubletalk. In this context, the affine projection algorithm (APA) and different versions of it are very attractive choices for AEC. However, an APA with a constant stepsize parameter has to compromise between the performance criteria 1) and 2). Therefore, a variable stepsize APA (VSSAPA) represents a more reliable solution. In this paper, we propose a VSSAPA derived in the context of AEC. Most of the APAs aim to cancel (i.e., projection order) previous a posteriori errors at every step of the algorithm. The proposed VSSAPA aims to recover the nearend signal within the error signal of the adaptive filter. Consequently, it is robust against nearend signal variations (including doubletalk). This algorithm does not require any a priori information about the acoustic environment, so that it is easy to control in practice. The simulation results indicate the good performance of the proposed algorithm as compared to other members of the APA family. Index Terms—Acoustic echo cancellation (AEC), adaptive filters, affine projection algorithm (APA), variable stepsize affine projection algorithm (VSSAPA). I.
An affine projection sign algorithm robust against impulsive interferences
 IEEE Signal Proces.s Lett
, 2010
"... Abstract—A new affine projection sign algorithm (APSA) is proposed, which is robust against nonGaussian impulsive interferences and has fast convergence. The conventional affine projection algorithm (APA) converges fast at a high cost in terms of computational complexity and it also suffers perf ..."
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Cited by 4 (1 self)
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Abstract—A new affine projection sign algorithm (APSA) is proposed, which is robust against nonGaussian impulsive interferences and has fast convergence. The conventional affine projection algorithm (APA) converges fast at a high cost in terms of computational complexity and it also suffers performance degradation in the presence of impulsive interferences. The family of sign algorithms (SAs) stands out due to its low complexity and robustness against impulsive noise. The proposed APSA combines the benefits of the APA and SA by updating its weight vector according to thenorm optimization criterion while using multiple projections. The features of the APA and thenorm minimization guarantee the APSA an excellent candidate for combatting impulsive interference and speeding up the convergence rate for colored inputs at a low computational complexity. Simulations in a system identification context show that the proposed APSA outperforms the normalized leastmeansquare (NLMS) algorithm, APA, and normalized sign algorithm (NSA) in terms of convergence rate and steadystate error. The robustness of the APSA against impulsive interference is also demonstrated. Index Terms—Adaptive filter, affine projection, sign algorithm. I.
A PROPORTIONATE AFFINE PROJECTION ALGORITHM USING FAST RECURSIVE FILTERING AND DICHOTOMOUS COORDINATE DESCENT ITERATIONS
"... Recently, a new proportionatetype APA called MIPAPA was developed, taking into account the “history ” of the proportionate factors. The use of a fast recursive filtering procedure together with the dichotomous coordinate descent (DCD) method is proposed for MIPAPA. Simulation results indicate tha ..."
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Cited by 4 (4 self)
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Recently, a new proportionatetype APA called MIPAPA was developed, taking into account the “history ” of the proportionate factors. The use of a fast recursive filtering procedure together with the dichotomous coordinate descent (DCD) method is proposed for MIPAPA. Simulation results indicate that the proposed algorithm has similar performance as the original algorithm with minimum performance losses.
A Family of Shrinkage AdaptiveFiltering Algorithms
, 2013
"... A family of adaptivefiltering algorithms that uses a variable step size is proposed. A variable step size is obtained by minimizing the energy of the noisefree a posteriori error signal which is obtained by using a known minimization formulation. Based on this methodology, a shrinkage affine pro ..."
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Cited by 2 (0 self)
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A family of adaptivefiltering algorithms that uses a variable step size is proposed. A variable step size is obtained by minimizing the energy of the noisefree a posteriori error signal which is obtained by using a known minimization formulation. Based on this methodology, a shrinkage affine projection (SHAP) algorithm, a shrinkage leastmeansquares (SHLMS) algorithm, and a shrinkage normalized leastmeansquares (SHNLMS) algorithm are proposed. The SHAP algorithm yields a significantly reduced steadystate misalignment as compared to the conventional affine projection (AP), variablestepsize AP, and setmembership AP algorithms for the same convergence speed although the improvement is achieved at the cost of an increase in the average computational effort per iteration in the amount of 11 % to 14%. The SHLMS algorithm yields a significantly reduced steadystate misalignment and faster convergence as compared to the conventional LMS and variablestepsize LMS algorithms. Similarly, the SHNLMS algorithm yields a significantly reduced steadystate misalignment and faster convergence as compared to the conventional normalized leastmeansquares (NLMS) and setmembership NLMS algorithms.
A SquareErrorBased Regularization for Normalized LMS Algorithms
"... Abstract—The purpose of a variable stepsize normalized LMS filter is to solve the dilemma of fast convergence or low steadystate error associated with the fixed regularized NLMS. By employing the inverse of weighted squareerror as the timevarying regularization parameter, we introduce a new regu ..."
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Abstract—The purpose of a variable stepsize normalized LMS filter is to solve the dilemma of fast convergence or low steadystate error associated with the fixed regularized NLMS. By employing the inverse of weighted squareerror as the timevarying regularization parameter, we introduce a new regularization for NLMS algorithms. Extensive simulation results demonstrate that our proposed algorithm outperforms existing schemes in speed of convergence, tracking ability, and low misadjustment. Index Terms—Adaptive filters, normalized least mean square (NLMS), variable stepsize NLMS, regularization parameter. I.
GAUSSSEIDEL BASED VARIABLE STEPSIZE AFFINE PROJECTION ALGORITHMS FOR ACOUSTIC ECHO CANCELLATION
"... Fast affine projection (FAP) algorithms have proved to be very attractive choice for acoustic echo cancellation (AEC). These algorithms offer a good tradeoff between convergence rate and computational complexity. Most of the existing FAP algorithms use a constant stepsize and need to compromise be ..."
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Fast affine projection (FAP) algorithms have proved to be very attractive choice for acoustic echo cancellation (AEC). These algorithms offer a good tradeoff between convergence rate and computational complexity. Most of the existing FAP algorithms use a constant stepsize and need to compromise between several performance criteria (e.g., fast convergence and low misalignment). In this paper, two FAP algorithms based on the GaussSeidel method and using a variable stepsize (VSS) are proposed for AEC. It is shown that the proposed algorithms are more robust against nearend signal variations (including doubletalk) than their counterparts that use a fixed stepsize. 1.