Title :
Least squares estimation of predictor coefficients from noisy observations
Author :
Tomcik, J.D. ; Melsa, J.L.
Author_Institution :
University of Notre Dame, Notre Dame, Indiana
Abstract :
A new method for estimating predictor coefficients (autoregressive parameters) based on noisy observations is presented. Least squares estimation methodology is used. Autoregressive parameters for the noisy observations are identified and used to find the desired autoregressive parameters. The particular application of concern is the digital processing of noisy speech.
Keywords :
Additive noise; Equations; Gaussian noise; Least squares approximation; Least squares methods; Signal analysis; Signal to noise ratio; Speech analysis; Speech processing; Uncertainty;
Conference_Titel :
Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications, 1977 IEEE Conference on
Conference_Location :
New Orleans, LA, USA
DOI :
10.1109/CDC.1977.271536
