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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0532-9
DOI :
10.1109/ICASSP.1992.226633
