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Journal ArticleDOI

Linear prediction: A tutorial review

John Makhoul
- Vol. 63, Iss: 4, pp 561-580
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TLDR
This paper gives an exposition of linear prediction in the analysis of discrete signals 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.
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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Citations
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Journal ArticleDOI

Continuous-time temporal back-propagation with adaptable time delays

TL;DR: Backpropagation is extended to continuous-time feedforward networks with internal, adaptable time delays, suitable for parallel hardware implementation, with continuous multidimensional training signals.
Journal ArticleDOI

A new algorithm for two-dimensional maximum entropy power spectrum estimation

TL;DR: A new iterative algorithm for the maximum entropy power spectrum estimation is presented, which utilizes the computational efficiency of the fast Fourier transform (FFT) algorithm and has been empirically observed to solve the maximum Entropy Power spectrum estimation problem.
Journal ArticleDOI

Automatic glottal inverse filtering from speech and electroglottographic signals

TL;DR: An automated on-line method to determine the glottal volume-velocity waveform from normal and pathological speech based on digital inverse filtering addresses the problems of accurate identification of vocal tract parameters and reduction of low-frequency noise.
Journal ArticleDOI

Human Problem Solving Performance in a Fault Diagnosis Task

TL;DR: It is proposed that humans in automated systems will be asked to assume the role of troubleshooter or problem solver and that the problems which they are asked to solve in such systems will not be amenable to rote solution.
Journal ArticleDOI

All-pole modeling of speech based on the minimum variance distortionless response spectrum

TL;DR: It is shown that MVDR modeling provides a class of all-pole models that are flexible for tackling a wide variety of speech modeling objectives and the high order MVDR spectrum provides a robust model for all types of speech including voiced speech, unvoiced speech, and mixed spectra.
References
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Journal ArticleDOI

A new look at the statistical model identification

TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Proceedings Article

Information Theory and an Extention of the Maximum Likelihood Principle

H. Akaike
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Book ChapterDOI

Information Theory and an Extension of the Maximum Likelihood Principle

TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.
Journal ArticleDOI

Singular value decomposition and least squares solutions

TL;DR: The decomposition of A is called the singular value decomposition (SVD) and the diagonal elements of ∑ are the non-negative square roots of the eigenvalues of A T A; they are called singular values.