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38
A nonparametric VSS NLMS algorithm
- IEEE Signal Process. Lett
, 2006
"... Abstract—The aim of a variable step size normalized least-mean-square (VSS-NLMS) algorithm is to try to solve the con-flicting requirement of fast convergence and low misadjustment of the NLMS algorithm. Numerous VSS-NLMS 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 least-mean-square (VSS-NLMS) algorithm is to try to solve the con-flicting requirement of fast convergence and low misadjustment of the NLMS algorithm. Numerous VSS-NLMS 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 VSS-NLMS-type algorithms. Second, a new nonparametric VSS-NLMS 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 steady-state 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 steady-state 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 step-size NLMS algorithm for under-modeling 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 under-modeling 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 under-modeling situation. Most of the adaptive algorithms for AEC were derived assuming an exact modeling scenario, so that they do not take into account the under-modeling noise. In this letter, a variable step-size nor-malized least-mean-square (VSS-NLMS) algorithm suitable for the under-modeling 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, nor-malized least mean square (NLMS), under-modeling system iden-tification, variable step-size NLMS. I.
Optimum variable explicit regularized affine projection algorithm
- in Proc. ICASSP-06
, 2006
"... Abstract—A variable regularized affine projection algorithm (VR-APA) 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 time-varying reg ..."
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Cited by 8 (5 self)
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Abstract—A variable regularized affine projection algorithm (VR-APA) 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 time-varying regularization param-eter is obtained by maximizing the speed of convergence of the algorithm. Although we first derive the VR-APA for a linear time invariant (LTI) system, we show that the same expression holds if we consider a time-varying system following a first-order Markov model. We also find expressions for the power of the steady-state error vector for the VR-APA and the standard APA with no regularization parameter. Particularly, we obtain quite different results with and without using the independence assumption be-tween 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 steady-state results, especially when high values of the projection order are used. Index Terms—Adaptive filtering, affine projection algorithm (APA), filtering, regularization, steady-state analysis. I.
A variable step-size affine projection algorithm designed for acoustic echo cancellation
- IEEE Trans. Audio, Speech, Lang. Process
, 2008
"... Abstract—The adaptive algorithms used for acoustic echo can-cellation (AEC) have to provide 1) high convergence rates and good tracking capabilities, since the acoustic environments imply very long and time-variant 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 can-cellation (AEC) have to provide 1) high convergence rates and good tracking capabilities, since the acoustic environments imply very long and time-variant echo paths, and 2) low misadjustment and robustness against background noise variations and double-talk. 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 step-size parameter has to compromise between the performance criteria 1) and 2). Therefore, a variable step-size APA (VSS-APA) represents a more reliable solution. In this paper, we propose a VSS-APA 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 VSS-APA aims to recover the near-end signal within the error signal of the adap-tive filter. Consequently, it is robust against near-end signal varia-tions (including double-talk). 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 per-formance of the proposed algorithm as compared to other mem-bers of the APA family. Index Terms—Acoustic echo cancellation (AEC), adaptive filters, affine projection algorithm (APA), variable step-size affine projec-tion algorithm (VSS-APA). 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 pro-posed, which is robust against non-Gaussian impulsive interfer-ences and has fast convergence. The conventional affine projection algorithm (APA) converges fast at a high cost in terms of compu-tational 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 pro-posed, which is robust against non-Gaussian impulsive interfer-ences and has fast convergence. The conventional affine projection algorithm (APA) converges fast at a high cost in terms of compu-tational complexity and it also suffers performance degradation in the presence of impulsive interferences. The family of sign algo-rithms (SAs) stands out due to its low complexity and robustness against impulsive noise. The proposed APSA combines the bene-fits of the APA and SA by updating its weight vector according to the-norm optimization criterion while using multiple pro-jections. The features of the APA and the-norm minimization guarantee the APSA an excellent candidate for combatting impul-sive 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 least-mean-square (NLMS) algorithm, APA, and normalized sign algorithm (NSA) in terms of convergence rate and steady-state 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 proportionate-type APA called MIPAPA was developed, taking into account the “history ” of the propor-tionate factors. The use of a fast recursive filtering proce-dure 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 proportionate-type APA called MIPAPA was developed, taking into account the “history ” of the propor-tionate factors. The use of a fast recursive filtering proce-dure together with the dichotomous coordinate descent (DCD) method is proposed for MIPAPA. Simulation results indicate that the proposed algorithm has similar perform-ance as the original algorithm with minimum performance losses.
A Family of Shrinkage Adaptive-Filtering Algorithms
, 2013
"... A family of adaptive-filtering algorithms that uses a variable step size is proposed. A variable step size is obtained by minimizing the energy of the noise-free 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 adaptive-filtering algorithms that uses a variable step size is proposed. A variable step size is obtained by minimizing the energy of the noise-free 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 least-mean-squares (SHLMS) algorithm, and a shrinkage normalized least-mean-squares (SHNLMS) algorithm are proposed. The SHAP algorithm yields a significantly reduced steady-state misalignment as compared to the conventional affine projection (AP), variable-step-size AP, and set-membership 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 steady-state misalignment and faster convergence as compared to the conventional LMS and variable-step-size LMS algorithms. Similarly, the SHNLMS algorithm yields a significantly reduced steady-state misalignment and faster convergence as compared to the conventional normalized least-mean-squares (NLMS) and set-membership NLMS algorithms.
A Square-Error-Based Regularization for Normalized LMS Algorithms
"... Abstract—The purpose of a variable step-size normalized LMS filter is to solve the dilemma of fast convergence or low steady-state error associated with the fixed regularized NLMS. By employing the inverse of weighted square-error as the time-varying regularization parameter, we introduce a new regu ..."
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Cited by 1 (1 self)
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Abstract—The purpose of a variable step-size normalized LMS filter is to solve the dilemma of fast convergence or low steady-state error associated with the fixed regularized NLMS. By employing the inverse of weighted square-error as the time-varying 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 step-size NLMS, regularization parameter. I.
GAUSS-SEIDEL BASED VARIABLE STEP-SIZE 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 trade-off between convergence rate and computational complexity. Most of the existing FAP algorithms use a constant step-size and need to compromise be ..."
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Cited by 1 (0 self)
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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 trade-off between convergence rate and computational complexity. Most of the existing FAP algorithms use a constant step-size and need to compromise between several performance criteria (e.g., fast convergence and low misalignment). In this paper, two FAP algorithms based on the Gauss-Seidel method and using a variable step-size (VSS) are proposed for AEC. It is shown that the proposed algorithms are more robust against nearend signal variations (including double-talk) than their counterparts that use a fixed step-size. 1.
