Geometric concepts and models in power spectral analysis

Tryphon T. Georgiou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present and contrast certain natural metrics between power spectral density functions of discrete-time random processes. We discuss two alternative viewpoints for quantifying uncertainty and distances between statistical models. We draw analogies with the paradigm of information geometry and the Fisher information metric, and make contact with the classical literature of linear prediction, the Kullback-Leibler distance, the Itakura-Saito distance, logarithmic L2-distances, and the total variation, which have been extensively used in applications of speech processing and pattern recognition.

Original languageEnglish (US)
Title of host publication15th Symposium on System Identification, SYSID 2009 - Preprints
Pages1529-1530
Number of pages2
EditionPART 1
DOIs
StatePublished - Dec 1 2009
Event15th IFAC Symposium on System Identification, SYSID 2009 - Saint-Malo, France
Duration: Jul 6 2009Jul 8 2009

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
Volume15
ISSN (Print)1474-6670

Other

Other15th IFAC Symposium on System Identification, SYSID 2009
CountryFrance
CitySaint-Malo
Period7/6/097/8/09

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