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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Linear prediction: A tutorial review – Semantic Scholar

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.

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The major part of aa paper is devoted to all-pole models. The model parameters are obtained by a least squares analysis in the time domain.

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Linear prediction: A tutorial review

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Least squares Search for additional papers on this topic. Topics Discussed in This Paper. Least squares Data compression Stationary process Arabic numeral 0.

Linear Prediction: A Tutorial Review

Quantization signal processing Spectral density Coefficient Noise shaping. Citations Publications citing this paper. Showing of 1, extracted citations.

Artificial bandwidth extension of narrowband speech-enhanced speech quality and intelligibility in mobile devices Laura Laaksonen Citation Statistics 3, Citations 0 ’76 ’86 ’97 ’08 ‘ Semantic Scholar estimates that this publication has 3, citations based on the available data. See our FAQ for additional information.

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A spectral characterization of the ill-conditioning in numerical deconvolution Michael P. On periodicity in series of related terms. 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 limear log mean and mean log averaging.

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