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.
Application the recursive extended least squares method for modeling a speech signal
In this article we will present a detailed study of recursive extended least squares method (RELS) to identify without bias of the models (system + disturbance). The idea is simultaneously to identify the model of the system and the model of the disturbance to obtain asymptotically a white error of prediction, which guarantees an unbiased estimate of the parameters system. Then we choose an application of this algorithm to identify the parameters of auto regressive model AR associated to the speech signal.
Physical Chemistry for the Life Sciences
PART 1 - BIOCHEMICAL THERMODYNAMICS PART 2- THE KINETICS OF LIFE PROCESSES PART 3 - BIOMOLECULAR STRUCTURE PART 4 - BIOMOLECULAR SPECTROSCOPY