DocumentCode
3243119
Title
Segmentation of nonstationary signals
Author
Djuric, Petar M. ; Kay, Steven M. ; Boudreaux-Bartels, G. Faye
Author_Institution
Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY, USA
Volume
5
fYear
1992
fDate
23-26 Mar 1992
Firstpage
161
Abstract
A very useful and not too restrictive class of models of nonstationary signals is based upon the assumptions that the signals are composed of independent and stationary segments that can be represented by autoregressive models. A usual task is then to find the number of segments of the observed signal, their boundaries, and the best model for each segment. A Bayesian solution to this task is proposed which does not require setting of any thresholds. The technical implementation of the solution is carried out via dynamic programming. The Monte Carlo simulations show excellent results
Keywords
signal processing; Bayesian solution; Monte Carlo simulations; autoregressive models; dynamic programming; nonstationary signal segmentation; observed signal; Bayesian methods; Biomedical engineering; Biomedical signal processing; Cost function; Density functional theory; Dynamic programming; Error correction; Signal analysis; Testing; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location
San Francisco, CA
ISSN
1520-6149
Print_ISBN
0-7803-0532-9
Type
conf
DOI
10.1109/ICASSP.1992.226633
Filename
226633
Link To Document