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Titolo: Cepstral coefficients, covariance lags, and polezero models for finite data strings
Autore: Byrnes, CI; Enqvist, P; Lindquist, A;
 Indirizzi:
 Washington Univ, Dept Syst Sci & Math, St Louis, MO 63130 USA Washington Univ St Louis MO USA 63130 Sci & Math, St Louis, MO 63130 USA Royal Inst Technol, Dept Math, Div Optimizat & Syst Theory, Stockholm, Sweden Royal Inst Technol Stockholm Sweden at & Syst Theory, Stockholm, Sweden
 Titolo Testata:
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
fascicolo: 4,
volume: 49,
anno: 2001,
pagine: 677  693
 SICI:
 1053587X(200104)49:4<677:CCCLAP>2.0.ZU;2M
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 IDENTIFICATION; REALIZATION; ALGORITHMS;
 Keywords:
 autoregressive moving average processes; cepstral analysis; covariance analysis; identification; maximum entropy methods; optimization methods; spectral analysis; speech analysis;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Engineering, Computing & Technology
 discip_EC
 Citazioni:
 40
 Recensione:
 Indirizzi per estratti:
 Indirizzo: Byrnes, CI Washington Univ, Dept Syst Sci & Math, St Louis, MO 63130 USA Washington Univ St Louis MO USA 63130 , St Louis, MO 63130 USA



 Citazione:
 C.I. Byrnes et al., "Cepstral coefficients, covariance lags, and polezero models for finite data strings", IEEE SIGNAL, 49(4), 2001, pp. 677693
Abstract
One of the most widely used methods of spectral estimation in signal and speech processing is linear predictive coding (LPC). LPC has some attractivefeatures, which account for its popularity, including the properties that the resulting modeling filter i) matches a finite window of n + 1 covariance lags, ii) is rational of degree at most n, and iii) has stable zeros and poles. The only limiting factor of this methodology is that the modeling filter is "allpole," i.e., an autoregressive (AR) model. In this paper, we present a systematic description of all autoregressive movingaverage (ARMA) models of processes that have properties i)iii) in the context of cepstral analysis and homomorphic filtering. Indeed, we show that each such ARMA model determines and is completely determined by its finite windows of cepstral coefficients and covariance lags. This characterization has an intuitively appealing interpretation of a characterization by using measures of the transient and the steadystate behaviors of the signal, respectively. More precisely, we show that these nthorder windows form local coordinates for all ARMA models of degree n and that the polezero model can be determined from the windows as the unique minimum of a convex objective function. We refine this optimization method by first noting that the maximum entropy design of an LPC filter is obtained by maximizing the zeroth cepstral coefficient, subject to the constraint i). More generally, we modify this scheme to a more wellposed optimization problem where the covariance data enters as a constraint and the linear weights of the cepstral coefficients are "positive"in a sense that a certain pseudopolynomial is positiverather succinctly generalizing the maximum entropy method. This newproblem is a homomorphic filter generalization of the maximum entropy method, providing a procedure for the design of any stable, minimumphase modeling filter of degree less or equal to n that interpolates the given covariance window We conclude the paper by presenting an algorithm for realizing these biters in a latticeladder form, given the covariance window and the moving average part of the model. While we also show how to determine the moving average part using cepstral smoothing, one can make use of any good a priori estimate for the system zeros to initialize the algorithm. Indeed, we conclude the paper with an example of this method, incorporating an example from the literature on ARMA modeling.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 02/04/20 alle ore 19:24:41