MAKHOUL LINEAR PREDICTION A TUTORIAL REVIEW PDF

Goltinris Least squares Search for additional papers on this topic. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License. On periodicity in series of related terms. Pole — zero modeling using cepstral prediction. Journal of Signal and Information ProcessingVol.

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Resources and Help Linear prediction: A tutorial review Abstract: This paper gives an exposition of linear prediction in the analysis of discrete signals.

The signal is modeled as a linear combination of its past values and present and past values of a hypothetical input to a system whose output is the given signal. In the frequency domain, this is equivalent to modeling the signal spectrum by a pole-zero spectrum. The major part of the paper is devoted to all-pole models. The model parameters are obtained by a least squares analysis in the time domain. Two methods result, depending on whether the signal is assumed to be stationary or nonstationary.

The same results are then derived in the frequency domain. The resulting spectral matching formulation allows for the modeling of selected portions of a spectrum, for arbitrary spectral shaping in the frequency domain, and for the modeling of continuous as well as discrete spectra.

This also leads to a discussion of the advantages and disadvantages of the least squares error criterion. A spectral interpretation is given to the normalized minimum prediction error.

Applications of the normalized error are given, including the determination of an "optimal" number of poles. The use of linear prediction in data compression is reviewed. For purposes of transmission, particular attention is given to the quantization and encoding of the reflection or partial correlation coefficients. Finally, a brief introduction to pole-zero modeling is given.

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Linear prediction: A tutorial review

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MAKHOUL LINEAR PREDICTION A TUTORIAL REVIEW PDF

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