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K Mayyas - One of the best experts on this subject based on the ideXlab platform.
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Performance analysis of the selective Coefficient Update NLMS algorithm in an undermodeling situation
Digital Signal Processing, 2013Co-Authors: K MayyasAbstract:The selective Coefficient Update normalized least mean-square (SCU-NLMS) algorithm was proposed to reduce computational complexity while preserving close performance to the full-Update NLMS algorithm, which brought it a lot of attention. In practical applications, the length of the unknown system impulse response is not known and, therefore, the length of the adaptive filter can be less than that of the unknown system particularly in situations when the unknown system impulse response is long. In all existing analysis of the SCU-NLMS algorithm, exact modeling of the unknown system is assumed, i.e., the length of the adaptive filter is equal to that of the unknown system impulse response. In this paper, we present mean-square performance analysis for the SCU-NLMS algorithm in an undermodeling situation and assuming independent and identically distributed (i.i.d.) input signals. The analysis model takes into account order statistics employed in the SCU-NLMS algorithm leading to accurate transient and steady state theoretical results. Analysis extends easily to the exact modeling case where expressions quantifying the algorithm mean-square performance are presented and shown to be more accurate than the ones reported in the literature. Simulation experiments validate the accuracy of the theoretical results in predicting the actual behavior of the algorithm.
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Low complexity LMS-type adaptive algorithm with selective Coefficient Update for stereophonic acoustic echo cancellation
Computers & Electrical Engineering, 2009Co-Authors: K MayyasAbstract:Stereophonic acoustic echo cancellation (SAEC) has brought up recently much attention and found a viable place in a number of hands-free applications. In this paper, we propose an LMS-type algorithm for SAEC based on decomposing the long adaptive filter of each channel of the SAEC system into smaller subfilters. We further reduce the complexity of the algorithm by employing the selective Coefficient Update (SCU) method in each subfilter. This leads to a significant improvement in the convergence rate of the algorithm with low computational overhead. However, the algorithm has a high final mean-square error (MSE) at steady-state that increases as number of subfilters increases. A combined-error algorithm is presented that achieves fast convergence without compromising the steady state error level. Simulations demonstrate the convergence speed advantages of the combined-error algorithm.
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reduced complexity transform domain adaptive algorithm with selective Coefficient Update
IEEE Transactions on Circuits and Systems Ii-express Briefs, 2004Co-Authors: K Mayyas, T AboulnasrAbstract:This paper proposes a new low-complexity transform-domain (TD) adaptive algorithm for acoustic echo cancellation. The algorithm is based on decomposing the long adaptive filter into smaller subfilters and employing the selective Coefficient Update (SCU) approach in each subfilter to reduce computational complexity. The resulting algorithm combines the fast converging characteristic of the TD decomposition technique and the benefits of the SCU of low complexity with minimal performance losses. The improvement in convergence speed comes at the expense of a corresponding increase in misadjustment. To overcome this problem, a hybrid of the proposed algorithm and the standard TD LMS algorithm (TDLMS) is presented. The hybrid algorithm retains the fast convergence speed capabilities of the original algorithm while allowing for low final MSE. Simulations show that the hybrid algorithm offers a superior performance when compared to the standard TDLMS algorithm with less computational overhead.
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complexity reduction of the nlms algorithm via selective Coefficient Update
IEEE Transactions on Signal Processing, 1999Co-Authors: T Aboulnasr, K MayyasAbstract:This article proposes an algorithm for partial Update of the Coefficients of the normalized least mean square (NLMS) finite impulse response (FIR) adaptive filter. It is shown that while the proposed algorithm reduces the complexity of the adaptive filter, it maintains the closest performance to the full Update NLMS filter for a given number of Updates. Analysis of the MSE convergence and steady-state performance for independent and identically distributed (i.i.d.) signals is provided for the extreme case of one Update/iteration.
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selective Coefficient Update of gradient based adaptive algorithms
International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: T Aboulnasr, K MayyasAbstract:One common approach to reducing the computational overhead of the normalized LMS (NLMS) algorithm is to Update a subset of the adaptive filter Coefficients. It is known that the mean square error (MSE) is not equally sensitive to the variations of the Coefficients. Accordingly, the choice of the Coefficients to be Updated becomes crucial. On this basis, we propose an algorithm that belongs to the same family but selects at each iteration a specific subset of the Coefficients that will result in the largest reduction in the performance error. The proposed algorithm reduces the complexity of the NLMS algorithm, as do the current algorithms from the same family, while maintaining a performance close to the full Update NLMS algorithm specifically for correlated inputs.
T Aboulnasr - One of the best experts on this subject based on the ideXlab platform.
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kalman based periodic Coefficient Update for fir adaptive filters
International Conference on Multimedia and Expo, 2007Co-Authors: N Avesta, T AboulnasrAbstract:This paper presents a novel partial Update algorithm for FIR adaptive filters based on a Kalman background engine. In the proposed system, a Kalman filter is setup with the Coefficients of the full adaptive filter as the states to be estimated. The observation of the Kalman filter is the subset of the Coefficients of the adaptive FIR filter being Updated. It is shown that this setup allows for an improved estimation of the full set of filter Coefficients despite the partial Update. We propose two methods for postmortem improvements on an ordinary M-Tap periodic Update LMS. We also propose a Kalman feedback method, in conjunction with a 1-Tap periodic Update TMS, which has a similar performance to a full length LMS, for non-stationary system identification.
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reduced complexity transform domain adaptive algorithm with selective Coefficient Update
IEEE Transactions on Circuits and Systems Ii-express Briefs, 2004Co-Authors: K Mayyas, T AboulnasrAbstract:This paper proposes a new low-complexity transform-domain (TD) adaptive algorithm for acoustic echo cancellation. The algorithm is based on decomposing the long adaptive filter into smaller subfilters and employing the selective Coefficient Update (SCU) approach in each subfilter to reduce computational complexity. The resulting algorithm combines the fast converging characteristic of the TD decomposition technique and the benefits of the SCU of low complexity with minimal performance losses. The improvement in convergence speed comes at the expense of a corresponding increase in misadjustment. To overcome this problem, a hybrid of the proposed algorithm and the standard TD LMS algorithm (TDLMS) is presented. The hybrid algorithm retains the fast convergence speed capabilities of the original algorithm while allowing for low final MSE. Simulations show that the hybrid algorithm offers a superior performance when compared to the standard TDLMS algorithm with less computational overhead.
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complexity reduction of the nlms algorithm via selective Coefficient Update
IEEE Transactions on Signal Processing, 1999Co-Authors: T Aboulnasr, K MayyasAbstract:This article proposes an algorithm for partial Update of the Coefficients of the normalized least mean square (NLMS) finite impulse response (FIR) adaptive filter. It is shown that while the proposed algorithm reduces the complexity of the adaptive filter, it maintains the closest performance to the full Update NLMS filter for a given number of Updates. Analysis of the MSE convergence and steady-state performance for independent and identically distributed (i.i.d.) signals is provided for the extreme case of one Update/iteration.
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selective Coefficient Update of gradient based adaptive algorithms
International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: T Aboulnasr, K MayyasAbstract:One common approach to reducing the computational overhead of the normalized LMS (NLMS) algorithm is to Update a subset of the adaptive filter Coefficients. It is known that the mean square error (MSE) is not equally sensitive to the variations of the Coefficients. Accordingly, the choice of the Coefficients to be Updated becomes crucial. On this basis, we propose an algorithm that belongs to the same family but selects at each iteration a specific subset of the Coefficients that will result in the largest reduction in the performance error. The proposed algorithm reduces the complexity of the NLMS algorithm, as do the current algorithms from the same family, while maintaining a performance close to the full Update NLMS algorithm specifically for correlated inputs.
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ICASSP - Selective Coefficient Update of gradient-based adaptive algorithms
1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: T Aboulnasr, K MayyasAbstract:One common approach to reducing the computational overhead of the normalized LMS (NLMS) algorithm is to Update a subset of the adaptive filter Coefficients. It is known that the mean square error (MSE) is not equally sensitive to the variations of the Coefficients. Accordingly, the choice of the Coefficients to be Updated becomes crucial. On this basis, we propose an algorithm that belongs to the same family but selects at each iteration a specific subset of the Coefficients that will result in the largest reduction in the performance error. The proposed algorithm reduces the complexity of the NLMS algorithm, as do the current algorithms from the same family, while maintaining a performance close to the full Update NLMS algorithm specifically for correlated inputs.
Mojtaba Lotfizad - One of the best experts on this subject based on the ideXlab platform.
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A Linear Neural Network-Based Approach to Stereophonic Acoustic Echo Cancellation
IEEE Transactions on Audio Speech and Language Processing, 2011Co-Authors: Mehdi Bekrani, Andy W. H. Khong, Mojtaba LotfizadAbstract: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.
Aykut Hocanin - One of the best experts on this subject based on the ideXlab platform.
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A 2-D recursive inverse adaptive algorithm
Signal Image and Video Processing, 2013Co-Authors: Mohammad Shukri Ahmad, Osman Kukrer, Aykut HocaninAbstract:In this paper, a 2-D form of the recently proposed recursive inverse (RI) adaptive algorithm is introduced. The filter Coefficients can be Updated along both the horizontal and vertical directions on a 2-D plane. The proposed approach uses a variable step size and avoids the use of the inverse autocorrelation matrix in the Coefficient Update equation, which leads to an improved and more stable performance. Performance of the 2-D RI algorithm is compared to that of the 2-D RLS algorithm in an image deconvolution and an adaptive line enhancer problem settings. The simulation results show that the proposed 2-D RI algorithm leads to an improved performance compared to that of the 2-D RLS algorithm.
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recursive inverse adaptive filtering algorithm
Digital Signal Processing, 2011Co-Authors: Mohammad Shukri Ahmad, Osman Kukrer, Aykut HocaninAbstract:In this paper, a new FIR adaptive filtering algorithm is proposed. The approach uses a variable step-size and the instantaneous value of the autocorrelation matrix in the Coefficient Update equation that leads to an improved performance. Convergence analysis of the algorithm has been presented. Simulation results show that the algorithm performs better than the Transform Domain LMS with Variable Step-Size (TDVSS) in stationary Additive White Gaussian Noise (AWGN) and Additive Correlated Gaussian Noise (ACGN) environments in a system identification setting. It is shown that the algorithm has a performance better than RLS and very similar to RRLS algorithm with a considerable reduction in computational complexity. Additionally, the performance of the proposed algorithm is shown to be superior to that of the Stabilized Fast Transversal Recursive Least Squares (SFTRLS) algorithm under the same conditions.
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recursive inverse adaptive filter with second order estimation of autocorrelation matrix
International Symposium on Signal Processing and Information Technology, 2010Co-Authors: Mohammad Shukri Ahmad, Osman Kukrer, Aykut HocaninAbstract:The recently proposed Recursive Inverse (RI) Adaptive Filtering algorithm uses a variable step-size and the first order recursive estimation of the correlation matrices in the Coefficient Update equation which lead to an improved performance. In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm uses the second order recursive estimation of the correlation matrices in the Coefficient Update equation which leads to an improved performance over the RI algorithm. The simulation results show that the algorithm outperforms the Transform Domain LMS with Variable Step-Size (TDVSS), the RI and the RLS algorithms in stationary environments. The performance of the algorithms is tested in Additive White Gaussian Noise (AWGN) and Correlated Noise environments.
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ISSPIT - Recursive inverse adaptive filter with second order estimation of autocorrelation matrix
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010Co-Authors: Mohammad Shukri Ahmad, Osman Kukrer, Aykut HocaninAbstract:The recently proposed Recursive Inverse (RI) Adaptive Filtering algorithm uses a variable step-size and the first order recursive estimation of the correlation matrices in the Coefficient Update equation which lead to an improved performance. In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm uses the second order recursive estimation of the correlation matrices in the Coefficient Update equation which leads to an improved performance over the RI algorithm. The simulation results show that the algorithm outperforms the Transform Domain LMS with Variable Step-Size (TDVSS), the RI and the RLS algorithms in stationary environments. The performance of the algorithms is tested in Additive White Gaussian Noise (AWGN) and Correlated Noise environments.
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recursive inverse adaptive filtering algorithm
Conference on Decision and Control, 2009Co-Authors: Mohammad Shukri Ahmad, Osman Kukrer, Aykut HocaninAbstract:In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm is based on the Quasi-Newton (QN) optimization algorithm. The approach uses a variable step-size in the Coefficient Update equation that leads to an improved performance. The simulation results show that the algorithm has very similar performance to the Robust Recursive Least Squares Algorithm (RRLS) while performing better than the Transform Domain LMS with Variable Step-Size (TDVSS) in stationary environments. The algorithm is tested in Additive White Gaussian Noise (AWGN) and Correlated Noise environments.
Masayuki Kawamata - One of the best experts on this subject based on the ideXlab platform.
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Realization of 2-D Variable IIR Digital Filter Structures with a Small Amount of Calculations for Coefficient Update
Multidimensional Systems and Signal Processing, 2004Co-Authors: Hyuk-jae Jang, Masayuki KawamataAbstract:This paper proposes 2-D variable IIR digital filter structures with a small amount of calculations for Coefficient Update. The proposed realization method uses the 2-D parallel allpass structure derived from the separable denominator 2-D filter as the prototype structure for 2-D variable digital filters. In order to reduce the amount of calculations, all the redundant first-order complex allpass sections are combined by modularization of the variable structure. Furthermore, we can realize a very compact variable structure with a minimal number of first-order complex allpass sections by combining complex allpass sections with their complex conjugate allpass sections. Comparison of the calculation loads of the variable structures is presented to demonstrate that the amount of calculations for Coefficient Update of the proposed variable structure is far less than that of the original and the modular variable structure.
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Bias Removal Algorithm for 2-D Equation Error Adaptive IIR Filters
Multidimensional Systems and Signal Processing, 1999Co-Authors: Maha Shadaydeh, Masayuki KawamataAbstract:This paper proposes a bias removal algorithm for equation error-based 2-D adaptive cascade IIR filters with separable denominator function. As well known, equation error-based adaptive IIR filtering algorithms have the advantages of fast convergence and unimodal mean-square-error surface. These advantages, however, come along with the drawback of biased parameter estimates in the presence of measurement noise. The adaptive filter structure in the proposed algorithm is based on the concept of backpropagating the desired signal through a cascade of the denominator vertical and horizontal sections. To handle the bias problem, the proposed algorithm uses a scaled value of the output error of each of the cascaded sections as an estimate for the measurement noise embedded in the signal part of the Coefficient-Update procedure of that section. Thus, while maintaining the advantages of easy stability monitoring, fast convergence, and low computational load, the effect of the measurement noise is suppressed. Input-Output stability analysis is carried out, and the constraints required to maintain stability are derived. Simulation examples are presented to support the effectiveness and the usability of the proposed bias removal algorithm in 2-D system identification and image enhancement applications.
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Realization of 2-D variable IIR digital filter structures with a small amount of calculations for Coefficient Update
The 2002 45th Midwest Symposium on Circuits and Systems 2002. MWSCAS-2002., 1Co-Authors: Hyuk-jae Jang, Masayuki KawamataAbstract:This paper proposes a 2-D variable IIR digital filter structure with a small amount of calculations for Coefficient Update. In the proposed realization method, the parallel allpass 2-D variable filter structure is modularized in order to reduce the number of first-order complex allpass sections. Then, one of the first-order complex allpass section pair in the modularized variable structure is replaced by the complex conjugate converter. By means of the proposed method, the variable structure with minimal hardware elements can be realized. Comparison of the amount of calculation between variable structures shows that the proposed variable structure requires less calculations for Coefficient Update than the conventional variable structure.