Orthogonal Adaptive Digital Filters with Applications to Acoustic System Identification

By Trevor P.B.M. Moat

Abstract:

The Transform-Domain LMS Algorithm
(Narayan, 1983) is studied in the
context of an acoustic system
identification problem. The power
estimator in this two-stage digital
filter is shown to affect the achievable
rates and depths of convergence
significantly. Preferred values for the
two tracking parameters, $\beta$ and
$\mu,$ are determined. Dynamic Step-size
Initialization is proposed to improve
early convergence by accelerating the
rate at which true power measurements
replace (arbitrary) initial values.
Later, linear estimators are shown to be
sub-optimal, particularly where the
spectral distribution of the reference
changes rapidly. A simple non-linear
Peak Window Power Estimator which
eliminates these problems is described.
It will be shown to improve the tracking
rates and misadjustment simultaneously.
The benefits of these methods are
demonstrated using FIR sequences
representative of typical acoustic
environments and using recordings from a
commercial telephone set. The proposed
structures surpass theexisting
algorithms consistently under all
circumstances tested.

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