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.
The model parameters are obtained by a least squares analysis in the time domain.
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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.
Linear prediction: A tutorial review
Optimal least squares time – domain synthesis of recursive digital filters. Pole – zero modeling using cepstral prediction.