Digital processing of speech signals

A solution is proposed to the long-standing problem of the numerical instability of fast recursive least squares transversal filter (FTF) algorithms with exponential weighting, an important class of algorithms for adaptive filtering. A framework for the analysis of the error propagation in FTF algorithms is first developed; within this framework, it is shown that the computationally most efficient 7N form is exponentially unstable. However, by introducing redundancy into this algorithm, feedback of numerical errors becomes possible; a judicious choice of the feedback gains then leads to a numerically stable FTF algorithm with a complexity of 8N multiplications and additions per time recursion. The results are presented for the complex multichannel joint-process filtering problem. >

Numerically stable fast transversal filters for recursive least squares adaptive filtering

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare “time bomb” has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.

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Artifact Removal in Physiological Signals—Practices and Possibilities

A unified view of algorithms for adaptive transversal FIR filtering and system identification has been presented. Wiener filtering and stochastic approximation are the origins from which all the algorithms have been derived, via a suitable choice of iterative optimization schemes and appropriate design parameters. Following this philosophy, the LMS algorithm and its offspring have been presented and interpreted as stochastic approximations of iterative deterministic steepest descent optimization schemes. On the other hand, the RLS and the quasi-RLS algorithms, like the quasi-Newton, the FNTN, and the affine projection algorithm, have been derived as stochastic approximations of iterative deterministic Newton and quasi-Newton methods. Fast implementations of these methods have been discussed. Block-adaptive, and block-exact adaptive filtering have also been considered. The performance of the adaptive algorithms has been demonstrated by computer simulations.

Efficient least squares adaptive algorithms for FIR transversal filtering

The hands-free telephone problem: an annotated bibliography

An effective method to stabilize fast RLS algorithms is proposed. It is based on the analysis of the propagation of the numerical errors according to a first-order linear model. Two variables are shown to be responsible for the numerical instability. The proposed method modifies the numerical properties of these variables, while preserving the theoretical form of the algorithms. This method is applied to the FTF (fast transversal filter) and fast Kalman algorithms. Experimental results in floating- and fixed-point arithmetic show the efficiency of the method. >

A new method to stabilize fast RLS algorithms based on a first-order of the propagation of numerical errors

A simplified FTF-type algorithm for adaptive filtering

SUMMARY

A new adaptive algorithm with fast convergence and low complexity is presented By using the calculation structure of the dual Kalman variables of the fast transversal filter algorithm and a simple decorrelating technique for the input signal, we obtain an algorithm that exhibits faster convergence speed and enhanced tracking ability compared with the normalized least-mean-square algorithm with similar computational complexity Copyright © 2013 John Wiley & Sons, Ltd

A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering

The authors propose a simple and efficient technique to improve the tracking capability of numerically stable fast, least-squares algorithms. The forgetting factor for the adaptation of the filtering part is implicitly modified, while the forgetting factor in the prediction part is kept to a value that ensures the numerical stability. A theoretical analysis of the modified algorithm is presented. Simulation results on a practical example (acoustic echo cancellation) show the efficiency of the proposed technique. >

Ahmed Benallal

Papers

A new method to stabilize fast RLS algorithms based on a first-order of the propagation of numerical errors

A simplified FTF-type algorithm for adaptive filtering

A fast convergence normalized least‐mean‐square type algorithm for adaptive filtering

Improvement of the tracking capability of the numerically stable fast RLS algorithms for adaptive filtering

A novel set membership fast NLMS algorithm for acoustic echo cancellation