Adaptive stack filtering by LMS and perceptron learning

Document Type

Conference Proceeding

Publication Date

1-1-1992

Abstract

Stack filters are a class of sliding-window nonlinear digital filters that possess the weak superposition property (threshold decomposition) and the ordering property known as the stacking property. They have been demonstrated to be robust in suppressing noise. Two methods are introduced to adaptively configure a stack filter. One is by employing the least mean square (LMS) algorithm and the other is based on perceptron learning. Experimental results are presented to demonstrate the effectiveness of the methods for noise suppression.

Identifier

77956837190 (Scopus)

ISBN

[0780305329]

Publication Title

ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings

External Full Text Location

https://doi.org/10.1109/ICASSP.1992.226412

ISSN

15206149

First Page

57

Last Page

60

Volume

4

This document is currently not available here.

Share

COinS