Linear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vect
While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to pr...
The aim of this paper is to provide an experimental evaluation of five linear prediction methods in ...
We shall present in this paper the results of investigations on the performance of Linear Prediction...
Linear prediction theory has had a profound impact in the field of digital signal processing. Althou...
We present an introduction to some aspects of digital signal processing and time series analysis whi...
We present a new method of analysis of the effects of data perturbations on the estimates of signal ...
The autocorrelation and covariance methods of linear prediction axe formulated in terms of an invers...
Heisey and Griffiths proposed a generalization of linear prediction, called ″linear estimation″ , in...
SIGLETIB: RN 2414 (328,1.2) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische Informatio...
The linear theory of prediction is capable of performing long term forecasting when the observed tim...
A generalisation of conventional linear prediction (LP) is proposed. The method implements the predi...
The Well-Known Analysis and Synthesis Filters of Linear Prediction Theory Are Extended Here to Inclu...
The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the ...
The autocorrelation and covariance methods of linear prediction are formulated in terms of an invers...
The application of linear prediction to frequency estimation for sinusoidal signals in noise is inve...
While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to pr...
The aim of this paper is to provide an experimental evaluation of five linear prediction methods in ...
We shall present in this paper the results of investigations on the performance of Linear Prediction...
Linear prediction theory has had a profound impact in the field of digital signal processing. Althou...
We present an introduction to some aspects of digital signal processing and time series analysis whi...
We present a new method of analysis of the effects of data perturbations on the estimates of signal ...
The autocorrelation and covariance methods of linear prediction axe formulated in terms of an invers...
Heisey and Griffiths proposed a generalization of linear prediction, called ″linear estimation″ , in...
SIGLETIB: RN 2414 (328,1.2) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische Informatio...
The linear theory of prediction is capable of performing long term forecasting when the observed tim...
A generalisation of conventional linear prediction (LP) is proposed. The method implements the predi...
The Well-Known Analysis and Synthesis Filters of Linear Prediction Theory Are Extended Here to Inclu...
The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the ...
The autocorrelation and covariance methods of linear prediction are formulated in terms of an invers...
The application of linear prediction to frequency estimation for sinusoidal signals in noise is inve...
While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to pr...
The aim of this paper is to provide an experimental evaluation of five linear prediction methods in ...
We shall present in this paper the results of investigations on the performance of Linear Prediction...