We have designed and tested an acoustic echo cancellation system for speech teleconferencing. Our algorithm is based on a least-mean-square (LMS) frequency domain adaptive filter (FDAF) and uses a novel filter-update technique using many (at least 3) simultaneously running filters. We find the new multi-filter to converge faster than similar LMS FDAF’s for echo cancellation, and find it to be e...

Inter-symbol interference if not taken care off may cause severe error at the receiver and the detection of signal becomes difficult. An adaptive equalizer employing Recursive Least Squares algorithm can be a good compensation for the ISI problem. In this paper performance of communication link in presence of Least Mean Square and Recursive Least Squares equalizer algorithm is analyzed. A Model...

This paper defines haw the quality of approximation used in the error transfer function analysis affects the accuracy of the resulting steady-state mean square error (MSE) of the Least-Mean-Square (LMS) adaptive filters. It shows that the error transfer function approach is very accurate for extremely narrow bandwidth input signals, and it deteriorates as the bandwidth of input signal increases.

The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with traditional dense-assumption based linear channel estimation methods, e.g., least mean square/fourth (LMS/F) algorithm, exploiting sparse structure information can get extra performance gain. By introducing -norm penalty...

A new reweighted l1-norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. Since standard LMS algorithm does not take into account the sparsity information about the channel impulse response (CIR), sparsity-aware modifications of the LMS algorithm aim at outperforming the standard LMS by introducing a penalty term to the standard LM...

The application of the least-mean-square (LMS) and recursive-least-square (RLS) algorithms to the estimation of symbol period is discussed. The algorithms are based on the measurements of time between two consecutive detected transitions in noisy waveforms. Two versions of the algorithm are developed, for white and colored measurement noise model. Conditions are derived that guarantee proper be...

In this paper, the speech signal is enhanced from the noisy speech signal using the proposed Least Mean Square (LMS) adaptive noise reduction algorithm. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is ...

This paper investigates the nonlinear effects of the LeastMean Square (LMS) adaptive predictor. Traditional analysis of the adaptive filter ignores the statistical dependence among successive tap-input vectors and bounds the performance of the adaptive filter by that of the finite-length Wiener filter. It is shown that the nonlinear effects make it possible for an adaptive transversal predictio...

In this paper, we propose two novel p-norm penalty least mean square (lp-LMS) algorithms as supplements of the conventional lp-LMS algorithm established for sparse adaptive filtering recently. A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error, w...