TY - GEN
T1 - Segmentation of nonstationary signals
AU - Djurić, Petar M.
AU - Kay, Steven M.
AU - Boudreaux-Bartels, G. Faye
N1 - Publisher Copyright: © 1992 IEEE.
PY - 1992
Y1 - 1992
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84999912268
U2 - 10.1109/ICASSP.1992.226633
DO - 10.1109/ICASSP.1992.226633
M3 - Conference contribution
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 161
EP - 164
BT - ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992
Y2 - 23 March 1992 through 26 March 1992
ER -