Pseudo Affine Projection Algorithms for Multichannel Active Noise Control (original) (raw)
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In the field of adaptive filtering, the fast implementations of affine projection algorithms are known to provide a good tradeoff between convergence speed and computational complexity. Such algorithms have recently been published for multichannel active noise control systems. Previous work reported that these algorithms can outperform more complex recursive least-squares algorithms when noisy plant models are used in active noise control systems. This paper proposes a new fast affine projection algorithm for multichannel active noise control or sound reproduction systems, based on the Gauss-Seidel solving scheme. The proposed algorithm has a lower complexity than the previously published algorithms, with the same convergence speed and the same good performance with noisy plant models, and a potential for better numerical stability. It provides the best performance/cost ratio. Details of the algorithm and its complexity are presented in the paper, with simulation results to validate its performance.
Steady-state performance of multichannel affine projection algorithms for active noise control
2008 16th European Signal Processing Conference, 2008
In the field of adaptive filtering, it is well known that affine projection (AP) algorithms lead to a good tradeoff between convergence speed and computational load. For multichannel sound control, some computationally efficient AP (fast AP) algorithms have been recently proposed. This paper analyzes the steady-state mean square error of two efficient affine projection algorithms (representative of the different approaches) for multichannel active noise control (ANC) applications based on a different filtering-x scheme: the modified filtered-x affine projection (MFXAP) algorithm (with the modified filtered-x structure embedded) and the filtered-x affine projection (FXAP) algorithm (based on the conventional filtered-x structure). This study depends on energy conservation arguments and does not require a specific signal distribution. The theoretical models derived allow to accurately predict the steady-state performance of the algorithms considered. Simulation results obtained in pra...
Multichannel fast affine projection algorithm for active noise Control
IEEE International Conference on Acoustics Speech and Signal Processing, 2002
In the field of adaptive signal processing, it is well known that fast affine projection algorithms can produce a good tradeoff between convergence speed and computational complexity. Although these algorithms typically do not provide the same convergence speed as recursive-leastsquares algorithms, they can provide a much improved convergence speed compared to stochastic gradient descent algorithms, without the high increase of the computational load or the instability often found in recursive-least-squares algorithms. In this paper, a multichannel fast affine projection algorithm is introduced for active noise control. The computational complexity of the new algorithm is evaluated, and it is shown through simulations that not only can the new algorithm provide the expected tradeoff between convergence performance and computational complexity, it can also provide the best convergence performance (even over recursive-leastsquares algorithms) when non-ideal noisy acoustic plant models are used in the adaptive systems.
AN EFFICIENT ALGORITHM FOR ACTIVE NOISE CONTROL
A multichannel filtered-x affine projection algorithm for active noise control (ANC) systems based on dichotomous coordinate descent (DCD) iterations is proposed. It is shown that it has better convergence properties, lower complexity, and improved robustness to inaccuracies of the plant model than other previously published algorithms for ANC systems.
The Journal of the Acoustical Society of America, 2008
In this paper, several multichannel modified filtered-x algorithms for active noise control systems using the dichotomous coordinate descent method (DCD) are introduced. This multiplier-less and division-less method is used for avoiding the matrix inversion that appears in adaptive algorithms such us recursive least square (RLS) based algorithms, affine projection (AP) or its fast versions. The study is focused on the important computational savings given by the use of DCD method, the effect on the convergence properties and stability of the investigated algorithms. A comparison of their convergence performance in case of using non-ideal noisy acoustic plants is also given. It is proved by simulations that the use of the dichotomous coordinate descent method can be an interesting option for reducing the computational cost of practical multichannel algorithms for ANC systems.
The paper extends the use of multichannel filtered-x affine projection algorithms suitable for feed-forward active noise control to a broad class of nonlinear filter structures, which comprises also Volterra filters and Functional Link Artificial Neural Networks (FLANN). An analysis of the transient and steady-state behavior of the resulting algorithms is provided. Some experimental results that compare multichannel filtered-x affine projection algorithms for Volterra filters and for FLANN are discussed.
IEEE Transactions on Speech and Audio Processing, 2003
In the field of adaptive signal processing, it is well known that affine projection algorithms or their low-computational implementations fast affine projection algorithms can produce a good tradeoff between convergence speed and computational complexity. Although these algorithms typically do not provide the same convergence speed as recursive-least-squares algorithms, they can provide a much improved convergence speed compared to stochastic gradient descent algorithms, without the high increase of the computational load or the instability often found in recursive-least-squares algorithms. In this paper, multichannel affine and fast affine projection algorithms are introduced for active noise control or acoustic equalization. Multichannel fast affine projection algorithms have been previously published for acoustic echo cancellation, but the problem of active noise control or acoustic equalization is a very different one, leading to different structures, as explained in the paper. The computational complexity of the new algorithms is evaluated, and it is shown through simulations that not only can the new algorithms provide the expected tradeoff between convergence performance and computational complexity, they can also provide the best convergence performance (even over recursive-least-squares algorithms) when nonideal noisy acoustic plant models are used in the adaptive systems.
An adaptive algorithms comparison for real multichannel active noise control
2004 12th European Signal Processing Conference, 2004
A comparative study of the multichannel Affine Projection (AP), the Fast Transversal Filter (FTF), the filtered-X LMS (FXLMS) and the Recursive Least Squares (RLS) algorithms is presented for active noise control (ANC) systems. This study is based on simulations using real data and laboratory experiments, and is focused on: their computational cost, their convergence properties, their stability and their ability to create quiet zones around listener ears. The performance of the AP algorithm in the real system suggests its use in ANC systems as an alternative to the classical multichannel FXLMS since it provides meaningful attenuation levels, lower convergence time and similar computational cost.
Optimal Regularization Parameter of the Multichannel Filtered-x Affine Projection Algorithm
IEEE Transactions on Signal Processing, 2007
We discuss the optimal regularization parameter of the Filtered-Affine Projection (FX-AP) algorithm suitable for feedforward active noise control. While the original FX-AP algorithm always provides a biased estimate of the minimum-meansquare solution, we show that the optimal regularized FX-AP algorithm is capable to eliminate the bias of the asymptotic solution and thus that the regularization parameter can optimize both the convergence speed and the residual MSE of the algorithm. We derive some expressions for the optimal regularization parameter, and we discuss some heuristic estimations of the optimal regularization parameter in practical conditions. Index Terms-Active noise control, affine projection algorithm, multichannel adaptive filtering, optimal regularization parameter.