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.
Statistical Signal Processing
Modern information systems must handle huge amounts of data having varied natural or technological origins. Automated processing of these increasing signal loads requires the training of specialists capable of formalising the problems encountered. This book supplies a formalised, concise presentation of the basis of statistical signal processing. Equal emphasis is placed on approaches related to signal modelling and to signal estimation. In order to supply the reader with the desirable theoretical fundamentals and to allow him to make progress in the discipline, the results presented here are carefully justified. The representation of random signals in the Fourier domain and their filtering are considered. These tools enable linear prediction theory and related classical filtering techniques to be addressed in a simple way. The spectrum identification problem is presented as a first step toward spectrum estimation, which is studied in non-parametric and parametric frameworks. The later chapters introduce synthetically further advanced techniques that will enable the reader to solve signal processing problems of a general nature. Rather than supplying an exhaustive description of existing techniques, this book is designed for students, scientists and research engineers interested in statistical signal processing and who need to acquire the necessary grounding to address the specific problems with which they may be faced. It also supplies a well-organized introduction to the literature.
Exponential stabilization of an axially moving string with geometrical nonlinearity by linear boundary feedback
This paper addresses the vibration suppression problem of an axially moving string using boundary control. Taking into account large transverse motion of the string via the second-order component of Lagrangian strain, a nonlinear oscillation equation is derived for the problem by employing the Hamilton's Principle. Direct Lyapunov method is then adopted to demonstrate that a linear boundary control of negative speed feedback ensures the vibration response of the moving string exponentially stable. The main contributions are a new stability condition in terms of controller gain parameter, and a detailed demonstration on the equilibrium condition at the boundary where the control is applied.