Adaptive nonlinear prediction based on order statistics for speech signals

Abstract

This paper proposes a novel adaptive algorithm for nonlinear prediction of speech signals, which turns out to be the adaptation procedure for an order statistic LMS predictor. The LMS-L lter Pitas et al. addressed is modied to preserve the time information in the input vector for the adaptation, in which a coe cient matrix is utilized to update the predictor coe cients. Computer simulations demonstrate that the novel nonlinear predictor provides better performance than the Volterra quadratic predictor as well as the linear predictor.

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Cite this paper

@inproceedings{Shimamura1999AdaptiveNP, title={Adaptive nonlinear prediction based on order statistics for speech signals}, author={Tetsuya Shimamura and Haruko Hayakawa}, booktitle={EUROSPEECH}, year={1999} }