Continuous adaptation of linear models with impulsive excitation

Author

Beet, S.W. ; Baghai-Ravary, L.

Author_Institution

Dept. of Electron. & Electr. Eng., Sheffield Univ., UK

Volume

4

fYear

1996

fDate

3-6 Oct 1996

Firstpage

2250

Abstract

Presents a new approach to continuously-adaptive system modelling, designed for the analysis of autoregressive (AR) systems excited by speech signals including an impulsive component. Voiced speech is well represented by such a model, and is used to demonstrate the advantages of the new approach. (1) AR model parameter estimates are more stable in the region of pitch events. (2) A faster adaptation rate can be used, reducing the recovery time after plosives or other sudden changes in signal statistics. The new method is based on multiple simultaneous estimates of each sample, using separate but related estimators. The general concept is illustrated using a linear prediction approach to continuously-adaptive AR modelling, based on the least mean square algorithm

Keywords

adaptive signal processing; adaptive systems; autoregressive processes; least mean squares methods; linear predictive coding; parameter estimation; speech coding; statistics; adaptation rate; continuously-adaptive autoregressive modelling; impulsive excitation; least mean square algorithm; linear models; linear prediction; multiple simultaneous estimates; pitch events; plosives; recovery time; signal statistics; speech signals; stable model parameter estimates; voiced speech; Adaptation model; Convergence; Equations; Least squares approximation; Parameter estimation; Predictive models; Signal analysis; Signal design; Speech; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-3555-4

Type

conf

DOI

10.1109/ICSLP.1996.607254

Filename

607254