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The composite vector stochastic processes model is usable in many signal processing areas. Advantages of the model utilization, in task of electric motors acoustic signals parametric estimations, are shown in this paper. Models' results are compared with the traditional statistical methods for the signal analysis, in the two samples classes recognition task. The expressions for correlation function,...
A new approach for a speech signal parametric spectral characterization, based on a new factorization method, is proposed in this paper. The theoretical basis of the factorization method for multimode speech signals spectra is presented in the paper. This method is based on the multiplicative polymodels developed by the authors. We also derived equations for autoregressive coefficients calculation...
The higher orders spectra analysis for non-Gaussian process spectrum estimation in the mix with Gaussian correlated interference are studied in the paper. The received expressions for parametric spectral estimation of higher orders are used for non-Gaussian processes spectra estimation that can be described by generic autoregressive models. The comparative analysis of the received parametric spectral...
The aim of the paper is to study the possibility of advancing noised non-Gaussian processes recognition using the features based on higher-order statistics. Several new recognition features based on the higher-order statistics, the basis of advancing recognition results in the presence of noise, decision rules and recognition system frameworks we proposed in the paper. The efficiency test of the proposed...
New linear prediction polymodels is described in the article. The structure of the universal linear prediction model is shown. We propose new additive linear prediction model AR1(2)+AR2(2). We also obtain spectral characteristics of additive processes and a system of equations for calculation of the additive model's parameters. Comparative analysis of resolution capability is made using four methods...
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