Efficient adaptive algorithm for modeling a speech signal
This paper presents a time-domain prediction error method based on Newton algorithm identification for the identification of auto regressive moving average (ARMA) models of linear structural systems. The proposed structural identification method can incorporate as much as possible structural information known a priori into the structural identification process to improve the identification accuracy. This method incorporates a recursive procedure that makes successive corrections in determining, a linear mathematical model, based on the data of observation, to represent the system considered. To avoid the practical difficulty often associated with bias prediction error measurement, a stochastic Newton method is proposed. The numerical results show that the proposed method is capable to obtain the minimal standard deviation of error prediction and the estimated parameters are closer with actual values. An application of a speech signal is provided then to identify the parameters of AR model corresponding to the speech signal.
Using recursive least squares estimator for modelling a speech signal
In this paper we will present a detailed study of recursive least squares method, and then we choose an application of this algorithm to identify the parameters of the autoregressive model AR associated to the speech signal. The technique proposed here makes it possible to identify the parameters of the autoregressive moving average model ARMA with unbiased estimates, this method is based on the minimization of a quadratic criterion.
Eimeria odocoilei n. sp. from the mule deer Odocoileus h. hemionus in Washington.
SYNOPSIS. Eimeria odocoilei n. sp. is described from feces of a mule deer Odocoileus h. hemionus in Washington. Its oocysts are 26 to 28 by 22 to 26 μ. It is the second species of Eimeria described from this host.