The Gauss-Seidel pseudo affine projection algorithm and its application for echo cancellation (original) (raw)
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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 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.
Modified Gauss-Seidel affine projection algorithm for acoustic echo cancellation
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2005
This paper proposes a robust and stable fast affine projection algorithm based on the Gauss-Seidel method, the so called modified Gauss-Seidel fast affine projection algorithm. The proposed algorithm is generalized for simplified Volterra filters as well. The computational complexity of both the modified Gauss-Seidel fast affine projection algorithm and its generalization for Simplified Volterra filters is derived and their performance for acoustic echo cancellation is assessed.
An Efficient Proportionate Affine Projection Algorithm for Echo Cancellation
IEEE Signal Processing Letters, 2000
Proportionate-type normalized least-mean-square algorithms were developed in the context of echo cancellation. In order to further increase the convergence rate and tracking, the "proportionate" idea was applied to the affine projection algorithm (APA) in a straightforward manner. The objective of this letter is twofold. First, a general framework for the derivation of proportionate-type APAs is proposed. Second, based on this approach, a new proportionate-type APA is developed, taking into account the "history" of the proportionate factors. The benefit is also twofold. Simulation results indicate that the proposed algorithm outperforms the classical one (achieving faster tracking and lower misadjustment). Besides, it also has a lower computational complexity due to a recursive implementation of the "proportionate history." Index Terms-Adaptive filtering, echo cancellation, proportionate affine projection algorithm.
A LOW COMPLEXITY PROPORTIONATE AFFINE PROJECTION ALGORITHM FOR ECHO CANCELLATION
Proportionate-type affine projection algorithms were developed in the context of echo cancellation, as a generalization of the proportionate-type normalized least-mean-square algorithms. A matrix inversion is required within the affine projection algorithm (APA). In the case of proportionatetype APAs, the update of the matrix to be inverted is very computationally expensive. In this paper, an efficient update of this matrix is proposed and the procedure is applied for a recently developed proportionate-type APA. It is shown that the proposed algorithm achieves similar performance but significantly lowers numerical complexity as compared to known proportionate-type APAs.
Robust variable step-size affine projection algorithm suitable for acoustic echo cancellation
The affine projection algorithm (APA) and different versions of it have proved to be very attractive choices for acoustic echo cancellation (AEC). In this context, a classical APA with a constant step-size has to compromise between two performance criteria, i.e., 1) high convergence rates and good tracking capabilities, and 2) low misadjustment and robustness against background noise variations and doubletalk. Consequently, a variable step-size APA (VSS-APA) is a more reliable solution. In this paper we propose a VSS-APA that is designed to recover the near-end signal from the error signal of the adaptive filter. Therefore, it is robust against near-end signal variations, including double-talk. Moreover, since it does not require a priori information about the acoustic environment, the proposed algorithm is easy to control in real-world AEC applications.
Set-membership affine projection algorithm for echo cancellation
This paper proposes a new data-selective affine projection algorithm for echo cancellation. The algorithm generalizes the concepts of the conventional set-membership affine-projection by incorporating a lower bound on the output error in order to prevent undesirable attenuation of the far-end signal. It is shown that the echo signal can more reliably be removed from the far-end user signals by employing the new algorithm in double talk situations. In addition, the proposed algorithm retains the fast convergence of the conventional SM-AP algorithm while keeping a reduced number of coefficient updates. Simulation results, using the ITU-T G.168 recommendation setup parameters, are presented in order to confirm the good features of the proposed algorithm.
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2007
We propose a gradient-limited affine projection algorithm (GL-APA), which can achieve fast and double-talk-robust convergence in acoustic echo cancellation. GL-APA is derived from the M-estimationbased nonlinear cost function extended for evaluating multiple error signals dealt with in the affine projection algorithm (APA). By considering the nonlinearity of the gradient, we carefully formulate an update equation consistent with multiple input-output relationships, which the conventional APA inherently satisfies to achieve fast convergence. We also newly introduce a scaling rule for the nonlinearity, so we can easily implement GL-APA by using a predetermined primary function as a basis of scaling with any projection order. This guarantees a linkage between GL-APA and the gradientlimited normalized least-mean-squares algorithm (GL-NLMS), which is a conventional algorithm that corresponds to the GL-APA of the first order. The performance of GL-APA is demonstrated with simulation results.
IEEE Signal Processing Letters, 2000
A straightforward generalization of the so-called affine projection algorithm (APA) to the multichannel (MC) case is easily obtained. However, due to the strong correlation between the input signals of the various channels, the resulting algorithm converges very slowly. This letter describes the way to overcome this problem and derives an efficient algorithm that turns out to make use of additional orthogonal projections.
Regularization of the Affine Projection Algorithm
IEEE Transactions on Circuits and Systems II: Express Briefs, 2000
The affine projection algorithm (APA) is an attractive choice for echo cancellation, mainly for its convergence features. A matrix inversion is required within the APA. For practical reasons, this matrix needs to be regularized, i.e., a positive constant is added to the elements of its main diagonal. This regularization parameter is of great importance in practice since, if it is not chosen properly, the APA may never converge, especially under low-signal-to-noise-ratio conditions. In this brief, we propose a formula for choosing the value of the regularization parameter, aiming at attenuating the effects of the noise in the adaptive filter estimate. Simulations performed in an acoustic echo cancellation scenario prove the validity of the approach in different noisy environments.