Estimation of the parameters of an autoregressive process in the presence of additive white noise

Update Item Information
Publication Type technical report
School or College College of Engineering
Department Computing, School of
Creator Done, William John
Title Estimation of the parameters of an autoregressive process in the presence of additive white noise
Date 1979
Description Applications of linear prediction (LP) algorithms have been successful in modeling various physical processes. In the area of speech analysis this has resulted in the development of LP vocoders, devices used in digital speech communication systems. The LP algorithms used in speech and other areas are based on all-pole models for the signal being considered. With white noise excitation to the model, the all-pole LP model is equivalent to the autoregressive (AR) model. With the success of this model for speech well established, the application of LP algorithms in noisy environments is being considered. Existing LP algorithms perform poorly in these conditions. Additive white noise severely effects the intelligibility and quality of speech after analysis by an LP vocoder.
Type Text
Publisher University of Utah
Subject Autoregressive process; Linear prediction algorithms; All-pole model
Subject LCSH White noise theory
Language eng
Bibliographic Citation Done, W. J. (1979). Estimation of the parameters of an autoregressive process in the presence of additive white noise. 1-207. UTEC-CSc-79-021.
Series University of Utah Computer Science Technical Report
Relation is Part of ARPANET
Rights Management ©University of Utah
Format Medium application/pdf
Format Extent 25,019,852 bytes
Identifier ir-main,16003
ARK ark:/87278/s6pn9q8z
Setname ir_uspace
ID 706643
Reference URL https://collections.lib.utah.edu/ark:/87278/s6pn9q8z