Low-complexity adaptive filtering algorithms based on the minimum L [infinity]-norm method

The complexity of an adaptive filtering algorithm is proportional to the tap length of the filter and hence, may become computationally prohibitive for applications requiring a long filter tap. In this thesis, we provide a framework for developing low-complexity adaptive filter algorithms by utilizing the concept of partial-updating along with the technique of finding the gradient vector in the hyperplane based on the L {592} -norm criterion. The resulting algorithm should have low-complexity not only because of the updating of only a subset of the filter coefficients at each time step, but also from the fact that updating a filter coefficient using the algorithm based on L {592} -norm requires less number of operations compared to the L 2 -norm algorithm. Two specific coefficient selection techniques, namely the sequential and M -Max coefficient selection techniques, are considered in this thesis. Statistical analyses of these two algorithms are carried out to derive the evolution equations for the mean and mean-square of the filter coefficient misalignment as well as to obtain stability bounds on the step-size of the two algorithms. Further, these analyses are used to show that the algorithm employing the M -Max coefficient selection technique can achieve a convergence rate that is closest to the full update algorithm. As a consequence, even though there are various ways of selecting a subset of the filter coefficients, the study of the other techniques becomes redundant. Simulations are carried out to validate the results obtained from the statistical analyses of the algorithms. The concept of developing algorithms based on the partial-updating and L {592} -norm is extended to proportionate adaptive filtering. Finally, the performance of the proposed adaptive filtering algorithms as well as that of the existing ones is studied in echo cancellation

Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms

Published versio

A wavelet based partial update fast LMS/Newton algorithm

This paper studies a wavelet based partial update fast LMS/Newton algorithm. Different from the conventional fast LMS/Newton algorithm, the proposed algorithm first uses a shorter-order, partial Haar transform-based NLMS adaptive filter to estimate the peak position of the long, sparse channel impulse response, and then employs the fast LMS/Newton algorithm integrated with partial update technique to fulfill the rest convergence task. The experimental results demonstrate the proposed algorithm outperforms its conventional counterpart in convergence performance and possesses a significantly lower computational complexity. © 2005 IEEE.published_or_final_versio

Sparseness-controlled adaptive algorithms for supervised and unsupervised system identification

In single-channel hands-free telephony, the acoustic coupling between the loudspeaker and the microphone can be strong and this generates echoes that can degrade user experience. Therefore, effective acoustic echo cancellation (AEC) is necessary to maintain a stable system and hence improve the perceived voice quality of a call. Traditionally, adaptive filters have been deployed in acoustic echo cancellers to estimate the acoustic impulse responses (AIRs) using adaptive algorithms. The performances of a range of well-known algorithms are studied in the context of both AEC and network echo cancellation (NEC). It presents insights into their tracking performances under both time-invariant and time-varying system conditions. In the context of AEC, the level of sparseness in AIRs can vary greatly in a mobile environment. When the response is strongly sparse, convergence of conventional approaches is poor. Drawing on techniques originally developed for NEC, a class of time-domain and a frequency-domain AEC algorithms are proposed that can not only work well in both sparse and dispersive circumstances, but also adapt dynamically to the level of sparseness using a new sparseness-controlled approach. As it will be shown later that the early part of the acoustic echo path is sparse while the late reverberant part of the acoustic path is dispersive, a novel approach to an adaptive filter structure that consists of two time-domain partition blocks is proposed such that different adaptive algorithms can be used for each part. By properly controlling the mixing parameter for the partitioned blocks separately, where the block lengths are controlled adaptively, the proposed partitioned block algorithm works well in both sparse and dispersive time-varying circumstances. A new insight into an analysis on the tracking performance of improved proportionate NLMS (IPNLMS) is presented by deriving the expression for the mean-square error. By employing the framework for both sparse and dispersive time-varying echo paths, this work validates the analytic results in practical simulations for AEC. The time-domain second-order statistic based blind SIMO identification algorithms, which exploit the cross relation method, are investigated and then a technique with proportionate step-size control for both sparse and dispersive system identification is also developed

A study on adaptive filtering for noise and echo cancellation.

The objective of this thesis is to investigate the adaptive filtering technique on the application of noise and echo cancellation. As a relatively new area in Digital Signal Processing (DSP), adaptive filters have gained a lot of popularity in the past several decades due to the advantages that they can deal with time-varying digital system and they do not require a priori knowledge of the statistics of the information to be processed. Adaptive filters have been successfully applied in a great many areas such as communications, speech processing, image processing, and noise/echo cancellation. Since Bernard Widrow and his colleagues introduced adaptive filter in the 1960s, many researchers have been working on noise/echo cancellation by using adaptive filters with different algorithms. Among these algorithms, normalized least mean square (NLMS) provides an efficient and robust approach, in which the model parameters are obtained on the base of mean square error (MSE). The choice of a structure for the adaptive filters also plays an important role on the performance of the algorithm as a whole. For this purpose, two different filter structures: finite impulse response (FIR) filter and infinite impulse response (IIR) filter have been studied. The adaptive processes with two kinds of filter structures and the aforementioned algorithm have been implemented and simulated using Matlab.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .J53. Source: Masters Abstracts International, Volume: 44-01, page: 0472. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005