A FAST Algorithm for Adaptive Filtering

Arezki, Madjid; Guessoum, Abderrezak; Meyrueis, Patrick

doi:10.1109/iwssip.2009.5367738

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…This Algorithm 2 is close to that proposed in [45][46][47], with the same differences as mentioned at the end of Algorithm 1 presentation. This Algorithm 2 is listed in Table V, and its computation complexity is given in Table VI.…”

Section: Lc1; T Lcnsupporting

confidence: 82%

“…We recall here that in [45][46][47], the authors proposed a comparable version to our Algorithm 1 with the same complexity (only in terms of multiplications). The differences between theses algorithms are as follows: (i) the proposed recursive relation of the likelihood variable .new/ L; t and (ii) the proposed periodic regularization of .new/ L; t that we propose in this paper.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

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New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation

Djendi

2014

Adaptive Control & Signal

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This paper addresses the field of stereophonic acoustic echo cancelation (SAEC) by adaptive filtering algorithms. Recently, simplified versions of the fast transversal filter (SFTF)-type algorithm has been proposed. In this paper, we propose two major contributions. In the first contribution, we propose two new FTF-type algorithms with low complexity and good convergence speed characteristics. These two proposed algorithms are mainly on the basis of a forward prediction scheme to estimate the so called dual Kalman gain, which is inherent in the filtering part update. This computation complexity is achieved by the introduction of new relations for the computation of the likelihood variables that are simple and lead to further simplifications on the prediction part of the two proposed algorithms. In the second contribution, we propose to adapt then apply these four new SFTF-type algorithms, (the two proposed algorithms in this paper and their original versions) in the SAEC applications. A fair comparison of the proposed algorithms with the original SFTF and the normalized least mean square algorithms, in mono and SAEC applications, is presented. Copyright 274 M. DJENDI Recently, a different approach for the SAEC problems (inter-channel coherence, adaptive filter lengths, etc.) was proposed in [18,19] by using the widely linear model, which is assigned to the processing of noncircular complex valued signals. A new hybrid solution to the same problem that uses frequency shifts to improve the performance of the state-of-art solution based on addition of half-wave rectified signals is presented in [20]. The authors in [21] proposed a windowing frequency domain adaptive filter, and an up-sampling block transforms preprocessing, to solve these constraints. The proposed adaptive filter uses windowing functions with smooth cutoff property to reduce the spectral leakage during filter updating, so the utilization of the independent noise introduced by preprocessing in SAEC can be increased. The proposed preprocessing is operated in short blocks with low processing delay, and it uses frequency-domain up-sampling to meet the minimal block length requirement given by the band limit of simultaneous masking. Another very recent and novel approach to SAEC is proposed in [22] and based on a combination of psychoacoustic effects. The principle of the proposed structure is to use an approach based on the 'missing-fundamental' phenomenon at low frequencies (below 500 Hz) and a modified phase modulation approach at higher frequencies.A basic scheme for SAEC is given in Figure 1, where we illustrate the concept with a transmission room on the left and a receiving room on the right [16,17,23,24]. In Figure 1, when we generate a signal in the transmission room, the two microphones mic 1 and mic 2 receive two sorts of signals: (i) the direct source signal modified by the path g 1 (then received by mic 1 / and g 2 (then received by mic 2 /, respectively, and (ii) the existing diffuse noises n 1 and n 2 in the transmission room and received ...

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Section: Lc1; T Lcnsupporting

confidence: 82%

Section: The Proposed Algorithmmentioning

confidence: 99%

New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation

Djendi

2014

Adaptive Control & Signal

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“…Such algorithms can reduce computational complexity and on the other hand perform close to the full-update methods in term of convergence rate and Mean Error Square (MSE). There are several variants of the LMS algorithms with partial update methods [3][4][5][6][7][8][9][10][11][12][13][14]. In addition, partial-update adaptive filters may suffer from stability or convergence problems when the input signal is cyclostationary or periodic [15].…”

Section: Introductionmentioning

confidence: 99%

A New Variable Length LMS Algorithm for Partial Update Adaptive Filtering Driven by Cyclostaionary Signal

Jamel¹,

Hammood²

2016

IJCDS

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The problem with some methods of Partial Update (PU) is that when using a special kind of input (Cyclostationary Signal), the system fails to converge or becomes unstable. These problems have been solved in this paper by using a newly proposed algorithm with two concepts of dynamic length Least Mean Square (LMS). The first is the length variation of the total filter coefficients N , while the second variation applies to the number of coefficients to be updated at each iteration M. This algorithm is called New Variable Length LMS algorithm NVLLMS. In the NVLLMS algorithm, the value of the Mean Error Square (MSE) is used to control N and M. Moreover, the step size is time varying. The proposed algorithm shows better performance compared with PU LMS algorithms through simulation results of system identification .

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“…In practice, it is therefore valuable to reduce the computational cost for real-time implementation. In previous works [2,3], we have already reduced the complexity of the fast transversal filter algorithm (FTF) by using only a reduced-size forward predictor to compute the adaptation gain with 2N+5P multiplications. In this paper, in order to find additional computational reductions, we use the same FTF algorithm where the adaptation gain is obtained by discarding completely both forward and backward predictors.…”

Section: Introductionmentioning

confidence: 99%

A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering

Benallal

Arezki

2013

Adaptive Control & Signal

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A new adaptive algorithm with fast convergence and low complexity is presented. By using the calculation structure of the dual Kalman variables of the fast transversal filter algorithm and a simple decorrelating technique for the input signal, we obtain an algorithm that exhibits faster convergence speed and enhanced tracking ability compared with the normalized least-mean-square algorithm with similar computational complexity.A. BENALLAL AND M. AREZKI CONCLUSIONIn this paper, a new FNLMS-type algorithm for adaptive filters is presented. The adaptation gain for the new algorithm is obtained by simplification of the FTF adaptation gain calculation. The amount of computation, 2N multiplications per sample, is the same for the proposed algorithm as for the NMLS algorithm. In all presented simulations, the proposed algorithm performs better than NLMS. It exhibits faster convergence and enhanced tracking ability compared with the NLMS algorithm.

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A FAST Algorithm for Adaptive Filtering

Cited by 3 publications

References 11 publications

New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation

New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation

A New Variable Length LMS Algorithm for Partial Update Adaptive Filtering Driven by Cyclostaionary Signal

A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering

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