Abstract: The convergence rate of the least-mean-square (LMS)
algorithm deteriorates if the input signal to the filter is correlated.
In a system identification problem, this convergence rate can be
improved if the signal is white and/or if the system is sparse. We
recently proposed a sparse transform domain LMS-type algorithm
that uses a variable step-size for a sparse system identification.
The proposed algorithm provided high performance even if the
input signal is highly correlated. In this work, we investigate the
performance of the proposed TD-LMS algorithm for a large number
of filter tap which is also a critical issue for standard LMS algorithm.
Additionally, the optimum value of the most important parameter is
calculated for all experiments. Moreover, the convergence analysis
of the proposed algorithm is provided. The performance of the
proposed algorithm has been compared to different algorithms in a
sparse system identification setting of different sparsity levels and
different number of filter taps. Simulations have shown that the
proposed algorithm has prominent performance compared to the other
algorithms.
Abstract: In this paper, a nonlinear acoustic echo cancellation
(AEC) system is proposed, whereby 3rd order Volterra filtering is
utilized along with a variable step-size Gauss-Seidel pseudo affine
projection (VSSGS-PAP) algorithm. In particular, the proposed
nonlinear AEC system is developed by considering a double-talk
situation with near-end signal variation. Simulation results
demonstrate that the proposed approach yields better nonlinear AEC
performance than conventional approaches.
Abstract: In this paper, a new pseudo affine projection (AP)
algorithm based on Gauss-Seidel (GS) iterations is proposed for
acoustic echo cancellation (AEC). It is shown that the algorithm is
robust against near-end signal variations (including double-talk).
Abstract: This paper introduces a new variable step-size APA with decorrelation of AR input process is based on the MSD analysis. To achieve a fast convergence rate and a small steady-state estimation error, he proposed algorithm uses variable step size that is determined by minimising the MSD. In addition, experimental results show that the proposed algorithm is achieved better performance than the other algorithms.
Abstract: This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projection matrix of pseudo-inverse to estimate system deviation. This method introduces temporal weights into the projection matrix, which is typically a better model of the real error's behavior than homogeneous temporal weights. The regularization overcomes the ill-conditioning introduced by both the forgetting process and the increasing size of the input matrix. This algorithm is tested by independent trials with coloured input signals and various parameter combinations. Results show that the proposed algorithm is superior in terms of convergence rate and misadjustment compared to existing algorithms. As a special case, a variable step size NLMS with forgetting factor is also presented in this paper.