Impulsive noise elimination using polynomial iteratively reweighted least squares

Author

Kuruoglu, Ercan E. ; Rayner, Peter J W ; Fitzgerald, William J.

Author_Institution

Dept. of Eng., Cambridge Univ., UK

fYear

1996

Firstpage

347

Lastpage

350

Abstract

A new nonlinear filtering technique is introduced for the elimination of impulsive noise modelled with a symmetric α-stable (SαS) distribution. The new algorithm, called polynomial iteratively reweighted least squares (PIRLS), employs a Volterra filter the coefficients of which are estimated by minimizing the l_p-norm of the estimation error. The filter, hence constructed, is used to estimate the clean data from the corrupted data. Simulation results obtained for audio data corrupted by synthetic SαS noise indicate that PIRLS is very successful in removing impulsive noise

Keywords

Volterra equations; acoustic signal processing; approximation theory; audio signals; error analysis; filtering theory; interference suppression; iterative methods; least mean squares methods; nonlinear filters; polynomials; statistical analysis; Volterra filter; audio data; clean data estimation; corrupted data; estimation error; filter coefficients; impulsive noise elimination; nonlinear filtering; polynomial iteratively reweighted least squares; simulation results; symmetric α-stable distribution; synthetic SαS noise; Acoustic noise; Electromagnetic interference; Filtering; Gaussian noise; Least squares approximation; Least squares methods; Polynomials; Signal processing; Signal processing algorithms; Wiener filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Workshop Proceedings, 1996., IEEE

Conference_Location

Loen

Print_ISBN

0-7803-3629-1

Type

conf

DOI

10.1109/DSPWS.1996.555532

Filename

555532