MAKHOUL LINEAR PREDICTION A TUTORIAL REVIEW PDF

The use of linear prediction in data compression is reviewed. For purposes of John Makhoul; Published in Proceedings of the IEEE. This paper gives an. Linear Prediction: A Tutorial Review. Authors: Makhoul, J. Publication: Proc. IEEE , Volume 63, p. Publication Date: 00/ Origin: GONG. Keywords. J. Makhoul, “Linear prediction A tutorial review,” Proc. IEEE, Vol. 63, pp. , Apr.

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Lineat to search form Skip to main content. 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.

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The model parameters are obtained by a least squares analysis in the time domain.

This paper has highly influenced other papers. This paper has 3, citations.

From This Paper Figures, tables, and topics from this paper. Least squares Search for additional papers on this topic.

Linear Prediction: A Tutorial Review

Topics Discussed in This Paper. Least squares Data compression Stationary process Arabic numeral 0. Quantization signal processing Spectral density Coefficient Noise shaping.

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A spectral characterization of the ill-conditioning in numerical deconvolution Michael P. On periodicity in series of related terms.

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Linear prediction: A tutorial review

Optimal least squares time – domain synthesis of recursive digital filters. Pole – zero modeling using cepstral prediction.

Recursion filters for digital processing. Analysis of the difference between log mean and mean log averaging. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.