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In this study, a modification to the affine projection adaptive filtering algorithm is proposed which is based on a three-level clipping of the input signal as applied to the weight update process. This clipping operation causes the input signal to be quantised into three levels, namely, 0, 1, and -1. By doing so, the proposed scheme, in addition to reducing the computational complexity, achieves an error convergence performance that is comparable to, or even at times better than that of the conventional affine projection algorithm (APA) for a certain range of the clipping threshold. The proposed adaptive algorithm is compared with some low complexity variants of APA with respect to the convergence rate and computational complexity, and its superiority to its counterparts of the same order of complexity in terms of the convergence rate is demonstrated. The mean square error analysis based on the energy conservation relation and the stability analysis is also presented for the proposed clipped APA.

The high computational load of conventional adaptive FIR filters applied to long system identification and their weak tracking ability has encouraged researchers to seek for efficient adaptive algorithms for this kind of applications. One of the efficient solutions is the three-level clipped LMS/RLS adaptive algorithm. In this paper, an insight into the performance of these adaptive algorithms that evaluates the amount of steady-state misalignment error of clipped LMS/RLS adaptive algorithms employed for identification of time-invariant and time-varying systems, is presented. Employing it, we compare the misalignment performance with their low-complexity adaptive algorithm counterparts theoretically. In addition, we derive the optimal step size/forgetting factor explicitly and also obtain a relation between the optimal level of the clipping and step size/forgetting factor to achieve the lowest steady-state misalignment and then we explain as to how to improve the performance of these kinds of adaptive algorithms by adjusting the clipping threshold according to the noise level in such a way that a higher performance is achieved. Finally, different adaptive algorithms have been further coded in VHDL in order to evaluate them in terms of speed and hardware resources.

Stereophonic acoustic echo cancellation remains one of the challenging areas for tele/video-conferencing applications. However, the existence of high interchannel coherence between the two input signals for such systems leads to considerable degradation in misalignment convergence of the adaptive filters. We propose a new algorithm for improving the convergence performance and steady-state misalignment by considering robustness to the source position in the transmission room. We achieve this by exploiting the inherent decorrelating properties of selective-tap adaptive filtering as well as employing a variable clipping threshold for the unselected taps. Simulation results using colored noise and speech signals show an improvement over existing algorithms both in terms of convergence rate as well as steady-state normalized misalignment.

We propose a new adaptive filtering algorithm for stereophonic acoustic echo cancellation. This algorithm uses a linear single-layer feedforward neural network to efficiently decorrelate the tap-input vectors. It achieves an improvement in the misalignment convergence by means of applying the resulted decorrelated tap-input vectors to the coefficient update of the adaptive filters. The advantage of our approach as compared with existing techniques is that our algorithm, in use with the nonlinear preprocessor, can achieve a high rate of misalignment convergence without significantly degrading the quality and stereophonic image of the transmitted signals since our neural network operates on the tap-input vectors as opposed to the transmitted audio signals. We then show that we can achieve an efficient implementation for the proposed decorrelation method by considering the structure of the joint-input covariance matrix of the stereophonic signals.

In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering was presented, have a nice tradeoff between complexity and the convergence speed. This paper describes a new approach for noise cancellation in speech enhancement using the two new adaptive filtering algorithms named fast affine projection algorithm and fast Euclidean direction search algorithms for attenuating noise in speech signals. The simulation results demonstrate the good performance of the two new algorithms in attenuating the noise., Comment: International Journal of Computer and Electrical Engineering, Vol. 2, No. 2, pp. 307-315, Apr. 2010. Singapore

Acoustic transmission for conferencing systems have progressed from the use of single channel to one that employs stereophonic channels. One of the most important challenges for such stereophonic system is the problem of stereophonic acoustic echo cancellation (SAEC) where a pair of echo cancellers are deployed to estimate the acoustic impulse responses of the receiving room. We propose, in this paper, a neural network based adaptive filtering approach for SAEC. The neural network is employed to decorrelate the input vectors for efficient filter updating, resulting in a high convergence rate of the adaptive filters for this multi-channel acoustic application. To further enhance the efficiency of the proposed algorithm, we then utilize the joint-input correlation matrix of the stereophonic signals so as to simplify the proposed neural network. Simulation results show the improvement in performance of the proposed adaptive SAEC approach over the state-of-the-art algorithms.

The use of partial-updating algorithm for reducing interchannel coherence in stereophonic acoustic echo cancellation has been proposed recently. In this work, we show that this algorithm suffers from the lack of robustness against source positions in the transmission room. To address this, we present an insight into this problem and propose a center clipping algorithm that improves the joint optimization between reducing interchannel coherence and increasing energies of selected taps for different source positions. Simulation results using both colored and speech inputs verify the robustness of the proposed algorithm against source positions.

Stereophonic acoustic echo cancellers (SAEC) have extended application in stereo teleconferencing systems. They have problems more severe than monophonic echo cancellers. The most important problem is the misalignment. This problem causes a severe divergence of filters in the event of abrupt changes of the transmission room acoustic paths. In this paper, we propose a novel wavelet thresholding approach for reducing the cross-correlation of the input vectors in the wavelet domain that leads to an improvement in the rate of misalignment reduction in adaptive filters employed in the stereophonic acoustic echo cancellation.

Stereophonic acoustic echo cancellers (SAEC) have problems more severe than monophonic echo cancellers. The most important problem is the misalignment, which is the mismatch between the impulse responses of adaptive filters and those of the acoustic paths of the receiving room (in the teleconferencing application, as an example) and therefore a severe divergence of filters in the event of abrupt changes of the transmission room acoustic paths. The main reason for such mismatching is the two-channel nature of the system and the high degree of cross-correlation of the input signals of those channels. In this paper, we try to reduce the correlation between the two channels in the transform domain. To achieve this, with inspiration from the lattice predictor structure, we present a novel approach for reduction of cross-correlation of the input vectors of two channels in the wavelet domain that lead to a reduction in the misalignment of adaptive filters for the SAEC.

A new algorithm is proposed for updating the weights of an adaptive filter. The proposed algorithm is a modification of an existing method, namely, the clipped LMS, and uses a three-level quantization ( ) scheme that involves the threshold clipping of the input signals in the filter weight update formula. Mathematical analysis shows the convergence of the filter weights to the optimum Wiener filter weights. Also, it can be proved that the proposed modified clipped LMS (MCLMS) algorithm has better tracking than the LMS algorithm. In addition, this algorithm has reduced computational complexity relative to the unmodified one. By using a suitable threshold, it is possible to increase the tracking capability of the MCLMS algorithm compared to the LMS algorithm, but this causes slower convergence. Computer simulations confirm the mathematical analysis presented.

This paper presents two new methods for designing digital filters using Fourier series expansion. The first method employs the impulse response of the filter, and the second method uses an FIR transform to obtain a digital filter from analog filter transfer function. Experimental results illustrate the accuracy of the methods.

Due to the good tracking behaviour of the LMS adaptive filter in a noisy environment, the FX-LMS algorithm is proposed in the literature as a method of active noise control, ANC. But each of the LMS and RLS algorithms have their own advantages and disadvantages. In this paper, a new approach based on a mixture of the RLS and LMS algorithms, RLMS, is presented. The optimum weights of the mixture are derived and it is proved that the MMSE of the proposed system is reduced compared to those of the RLS and LMS algorithms. Then, the proposed RLMS algorithm is employed for active noise cancellation to form the FX-RLMS algorithm, in a duct. Experimental results show better performance of the RLMS algorithm compared to both the RLS and LMS algorithms of convergence and tracking behaviour in the system identification problem and noisy chirp tracking. The FX-RLMS algorithm shows better results in active noise cancellation compared to the FX-LMS algorithm.

The tracking algorithm is an important tool for motion analysis in computer vision. A new car tracking algorithm is proposed which is based on a new clipping technique in the field of adaptive filter algorithms. The uncertainty and occlusion of vehicles increase the noise in vehicle tracking in a traffic scene, so the new clipping technique can control noise in prediction of vehicle positions. The authors present a new quantised version of the LMS, namely the QX-LMS algorithm, which has a better tracking capability in comparison with the clipped LMS (CLMS) and the LMS and also involves less computation. The threshold parameter of the QX-LMS algorithm causes controllability and the increase of tracking and convergence properties, whereas the CLMS and LMS algorithms do not have these capabilities. The QX-LMS algorithm is used for estimation of a noisy chirp signal, for system identification and in car tracking applications. Simulation results for noisy chirp signal detection show that this algorithm yields a considerable error reduction in comparison to the LMS and CLMS algorithms. The proposed algorithm, in tracking some 77 vehicles in different traffic scenes, shows a reduction of the tracking error relative to the LMS and CLMS algorithms.