Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
An AR Model Identification Method using a Prior Information and Its Application to a Spectral Estimation of Speech Signal
An Extension of the Burg Method Based on the Principle of the Minimum Cross Entropy
Ken NAKAMUROTeruyo WADASueo SUGIMOTO
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2005 Volume 18 Issue 5 Pages 171-177

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Abstract

A novel Auto Regressive (AR) model parameter estimation method is proposed, which can utilize a prior information as well as time series data, by extending the Burg method on the basis of the Minimum Cross Entropy (MCE) principle. As a practical application of the proposed method, we consider an approach to spectral estimation of speech data. In general, effectiveness of a prior information to spectral estimation results depends on the variation of speech signal. Thus we introduce an algorithm to determine the usage of a prior information, based on the divergence measure defined by the Kullback information. Finally, the estimation results for real speech data illustrate improved performance in comparison to the Burg method.

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