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Analysis of Transient and Steady-State Behavior of a Multichannel Filtered-x Partial-Error Affine Projection Algorithm  [cached]
Alberto Carini,Giovanni L. Sicuranza
EURASIP Journal on Audio, Speech, and Music Processing , 2007, DOI: 10.1155/2007/31314
Abstract: The paper provides an analysis of the transient and the steady-state behavior of a filtered-x partial-error affine projection algorithm suitable for multichannel active noise control. The analysis relies on energy conservation arguments, it does not apply the independence theory nor does it impose any restriction to the signal distributions. The paper shows that the partial-error filtered-x affine projection algorithm in presence of stationary input signals converges to a cyclostationary process, that is, the mean value of the coefficient vector, the mean-square error and the mean-square deviation tend to periodic functions of the sample time.
Analysis of Transient and Steady-State Behavior of a Multichannel Filtered-x Partial-Error Affine Projection Algorithm  [cached]
Carini Alberto,Sicuranza Giovanni L
EURASIP Journal on Audio, Speech, and Music Processing , 2007,
Abstract: The paper provides an analysis of the transient and the steady-state behavior of a filtered-x partial-error affine projection algorithm suitable for multichannel active noise control. The analysis relies on energy conservation arguments, it does not apply the independence theory nor does it impose any restriction to the signal distributions. The paper shows that the partial-error filtered-x affine projection algorithm in presence of stationary input signals converges to a cyclostationary process, that is, the mean value of the coefficient vector, the mean-square error and the mean-square deviation tend to periodic functions of the sample time.
Filtered-X Affine Projection Algorithms for Active Noise Control Using Volterra Filters  [cached]
Giovanni L. Sicuranza,Alberto Carini
EURASIP Journal on Advances in Signal Processing , 2004, DOI: 10.1155/s1687617204401164
Abstract: We consider the use of adaptive Volterra filters, implemented in the form of multichannel filter banks, as nonlinear active noise controllers. In particular, we discuss the derivation of filtered-X affine projection algorithms for homogeneous quadratic filters. According to the multichannel approach, it is then easy to pass from these algorithms to those of a generic Volterra filter. It is shown in the paper that the AP technique offers better convergence and tracking capabilities than the classical LMS and NLMS algorithms usually applied in nonlinear active noise controllers, with a limited complexity increase. This paper extends in two ways the content of a previous contribution published in Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03), Grado, Italy, June 2003. First of all, a general adaptation algorithm valid for any order L of affine projections is presented. Secondly, a more complete set of experiments is reported. In particular, the effects of using multichannel filter banks with a reduced number of channels are investigated and relevant results are shown.
Sparsity-Aware Filtered-X Affine Projection Algorithms for Active Noise Control  [PDF]
A. Gully,R. C. de Lamare
Computer Science , 2014,
Abstract: This paper describes a novel technique for promoting sparsity in the modified filtered-x algorithms required for active noise control. The proposed algorithms are based on recent techniques incorporating approximations to the \ell_0-norm in the cost functions that are used to derive adaptive filtering algorithms. In particular, zero-attracting and reweighted zero-attracting filtered-x adaptive algorithms are developed and considered for active noise control problems. The results of simulations indicate that the proposed techniques improve the convergence of the existing modified algorithm in the case where the primary and secondary paths exhibit a degree of sparsity.
Adaptive Equalizer Using Selective Partial Update Algorithm and Selective Regressor Affine Projection Algorithm over Shallow Water Acoustic Channels  [PDF]
Masoumeh Soflaei,Paeiz Azmi
Shock and Vibration , 2014, DOI: 10.1155/2014/676497
Abstract: One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI) which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA) and the family of selective partial update APA (SPU-APA) which have low computational complexity that is one of the important factors that influences adaptive equalizer performance. We apply experimental data from Strait of Hormuz for examining the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA and SPU-APA decrease by 5.8?(dB) and 5.5?(dB), respectively, in comparison with least mean square (LMS) algorithm. Also the families of SPU-APA and SR-APA have better convergence speed than LMS type algorithm. 1. Introduction Underwater acoustic communications suffer from doppler shifts, noise, scattering from surface, and reflecting from bottom that cause multipath spread especially in shallow water channels. Multipath spread causes intersymbol interference (ISI), which influences reliable transmission in underwater acoustic communications. One of the best ways suggested for overcoming this effect is using adaptive equalization methods in receivers. Some factors such as adaptive filter and adaptive algorithms influence this method, so it is important to choose adaptive algorithms with low computational complexity and more ability for tracking changes in channel. Least mean square (LMS) algorithm is the most popular because of its simplicity in computations and implementation that is used by Stojanovic et al. [1, 2] in underwater acoustic communication. Recursive least square (RLS) due to the fastest convergence speed is the best adaptive filter algorithm used by Zheng et al. [3] and Freitag et al. [4] in this channel in spite of its computational complexity. Computational complexity in some algorithms such as RLS is one of the main problems, so some other algorithms such as selective regressor affine projection algorithm (SR-APA) and selective partial update (SPU) that have good tradeoff between computational complexity and convergence speed are suggested. In SPU algorithm, the blocks of filter coefficients have been updated in every iteration that is selected by special criteria. Important examples of this algorithm are different types of selective partial update normalized least mean
Detection-Guided Fast Affine Projection Channel Estimator for Speech Applications  [cached]
Jennifer Yan Wu,Homer John,Rombouts Geert,Moonen Marc
EURASIP Journal on Audio, Speech, and Music Processing , 2007,
Abstract: In various adaptive estimation applications, such as acoustic echo cancellation within teleconferencing systems, the input signal is a highly correlated speech. This, in general, leads to extremely slow convergence of the NLMS adaptive FIR estimator. As a result, for such applications, the affine projection algorithm (APA) or the low-complexity version, the fast affine projection (FAP) algorithm, is commonly employed instead of the NLMS algorithm. In such applications, the signal propagation channel may have a relatively low-dimensional impulse response structure, that is, the number m of active or significant taps within the (discrete-time modelled) channel impulse response is much less than the overall tap length n of the channel impulse response. For such cases, we investigate the inclusion of an active-parameter detection-guided concept within the fast affine projection FIR channel estimator. Simulation results indicate that the proposed detection-guided fast affine projection channel estimator has improved convergence speed and has lead to better steady-state performance than the standard fast affine projection channel estimator, especially in the important case of highly correlated speech input signals.
Detection-Guided Fast Affine Projection Channel Estimator for Speech Applications  [cached]
Yan Wu Jennifer,John Homer,Geert Rombouts,Marc Moonen
EURASIP Journal on Audio, Speech, and Music Processing , 2007, DOI: 10.1155/2007/71495
Abstract: In various adaptive estimation applications, such as acoustic echo cancellation within teleconferencing systems, the input signal is a highly correlated speech. This, in general, leads to extremely slow convergence of the NLMS adaptive FIR estimator. As a result, for such applications, the affine projection algorithm (APA) or the low-complexity version, the fast affine projection (FAP) algorithm, is commonly employed instead of the NLMS algorithm. In such applications, the signal propagation channel may have a relatively low-dimensional impulse response structure, that is, the number m of active or significant taps within the (discrete-time modelled) channel impulse response is much less than the overall tap length n of the channel impulse response. For such cases, we investigate the inclusion of an active-parameter detection-guided concept within the fast affine projection FIR channel estimator. Simulation results indicate that the proposed detection-guided fast affine projection channel estimator has improved convergence speed and has lead to better steady-state performance than the standard fast affine projection channel estimator, especially in the important case of highly correlated speech input signals.
Kernel Affine Projection Algorithms  [cached]
Weifeng Liu,José C. Príncipe
EURASIP Journal on Advances in Signal Processing , 2008, DOI: 10.1155/2008/784292
Abstract: The combination of the famed kernel trick and affine projection algorithms (APAs) yields powerful nonlinear extensions, named collectively here, KAPA. This paper is a follow-up study of the recently introduced kernel least-mean-square algorithm (KLMS). KAPA inherits the simplicity and online nature of KLMS while reducing its gradient noise, boosting performance. More interestingly, it provides a unifying model for several neural network techniques, including kernel least-mean-square algorithms, kernel adaline, sliding-window kernel recursive-least squares (KRLS), and regularization networks. Therefore, many insights can be gained into the basic relations among them and the tradeoff between computation complexity and performance. Several simulations illustrate its wide applicability.
Set-Membership Proportionate Affine Projection Algorithms  [cached]
Werner Stefan,Apolinário José A,Diniz Paulo SR
EURASIP Journal on Audio, Speech, and Music Processing , 2007,
Abstract: Proportionate adaptive filters can improve the convergence speed for the identification of sparse systems as compared to their conventional counterparts. In this paper, the idea of proportionate adaptation is combined with the framework of set-membership filtering (SMF) in an attempt to derive novel computationally efficient algorithms. The resulting algorithms attain an attractive faster converge for both situations of sparse and dispersive channels while decreasing the average computational complexity due to the data discerning feature of the SMF approach. In addition, we propose a rule that allows us to automatically adjust the number of past data pairs employed in the update. This leads to a set-membership proportionate affine projection algorithm (SM-PAPA) having a variable data-reuse factor allowing a significant reduction in the overall complexity when compared with a fixed data-reuse factor. Reduced-complexity implementations of the proposed algorithms are also considered that reduce the dimensions of the matrix inversions involved in the update. Simulations show good results in terms of reduced number of updates, speed of convergence, and final mean-squared error.
Set-Membership Proportionate Affine Projection Algorithms  [cached]
Stefan Werner,José A. Apolinário,Paulo S. R. Diniz
EURASIP Journal on Audio, Speech, and Music Processing , 2007, DOI: 10.1155/2007/34242
Abstract: Proportionate adaptive filters can improve the convergence speed for the identification of sparse systems as compared to their conventional counterparts. In this paper, the idea of proportionate adaptation is combined with the framework of set-membership filtering (SMF) in an attempt to derive novel computationally efficient algorithms. The resulting algorithms attain an attractive faster converge for both situations of sparse and dispersive channels while decreasing the average computational complexity due to the data discerning feature of the SMF approach. In addition, we propose a rule that allows us to automatically adjust the number of past data pairs employed in the update. This leads to a set-membership proportionate affine projection algorithm (SM-PAPA) having a variable data-reuse factor allowing a significant reduction in the overall complexity when compared with a fixed data-reuse factor. Reduced-complexity implementations of the proposed algorithms are also considered that reduce the dimensions of the matrix inversions involved in the update. Simulations show good results in terms of reduced number of updates, speed of convergence, and final mean-squared error.
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