Improvement of the convergence speed and the tracking ability of the fast Newton type adaptive filtering (FNTF) algorithm
ABSTRACT In this paper, five new versions of the fast Newton type adaptive filtering (FNTF) algorithm are presented. A brief preliminary presentation of these algorithms was given in Djendi et al. [Comparative study of new version of the Newton type adaptive filtering algorithm, in: Proceedings of the IEEE ICASSP 2004, Montreal, Canada, May 2004, pp. 677–680]. The first algorithm is based on a simple modification of the filtering part, by introducing a scalar accelerator parameter. The second algorithm is based on the use of the temporal subdivision technique to update the local filter coefficients. The third algorithm is a modification of the second one, by the use of the final filtering errors to update the filter coefficients. The fourth and the fifth algorithms are based, respectively, on the combination of features from the first algorithm with features of the second and third algorithms. These new algorithms are proposed to improve the convergence speed of the original version of the FNTF algorithm for the identification of acoustic impulse responses, and also to improve the tracking ability when the systems vary in time. A comparative study of each algorithm with the original version of the FNTF algorithm is also presented.
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ABSTRACT: This paper addresses the problem of acoustic echo cancellation. We propose a new version of the fast Newton transversal filter algorithm for stereophonic acoustic echo cancellation applications. This algorithm can be viewed as an efficient implementation of the extended two-channel fast transversal filter algorithm. Moreover, it fits naturally within the frame of the fast version of the recursive least-squares (RLS) algorithm, applied to the two-channel case. To stabilize the proposed two-channel algorithm, we have adapted and then applied a new numerical stabilization technique that has been proposed recently. The computational complexity of the proposed two-channel algorithm is less than half the complexity of the fastest two-channel RLS versions and very close to that of the two-channel normalized least mean squares algorithm when its predicting part length is chosen to be small. Simulation results and comparisons in term of complexities, convergence speed and tracking with the two-channel algorithms are presented. Copyright © 2009 John Wiley & Sons, Ltd.International Journal of Adaptive Control and Signal Processing 07/2009; 24(6):435 - 444. · 1.22 Impact Factor
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ABSTRACT: This paper addresses the problem of acoustic noise reduction and speech enhancement by adaptive filtering algorithms. Most speech enhancement methods and algorithms which use adaptive filtering structure are generally expressed in fullband form. One of these widespread structures is the Forward Blind Source Separation Structure (FBSS). This FBSS structure is often used to separate speech form noise and therefore enhance the speech signal at the processing output. In this paper, we propose a new subband implementation of this FBSS structure. In order to give more robustness to the proposed structure, we adapt then we apply to this subband structure a new combination of criteria based on the system mismatch and the smoothing filtering errors minimizations. The combination between this proposed subband structure with this optimal criteria allows to obtain a new two-channel subband forward (2CSF) algorithm that improves the convergence speed of the cross adaptive filters which are used to separate speech from noise. Objective tests under various environments are presented showing the good behavior of the proposed 2CSF algorithm.Computers & Electrical Engineering 01/2013; 39(8):2531–2550. · 0.99 Impact Factor
- Applied Acoustics 12/2014; 86:25–41. · 1.07 Impact Factor