By Topic

Non-stationary signal analysis using temporal clustering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
S. Policker ; Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel ; A. B. Geva

We present a model of nonstationary time series generated by switching between a finite number of random processes and apply temporal clustering to estimate the model's parameters. Applications of the algorithm to segmentation of nonstationary time series and a simple example of preprocessing a speech signal will be discussed. The model defines a nonstationary composite source generated by randomly switching between elements of a finite number of random processes. The switching probability distribution which underlies the behavior of the switch is controlled by a time varying vector of parameters which is used to determine a different switching probability in each time instant. This definition allows us to analyze a drift between disjoint states of the composite model

Published in:

Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop

Date of Conference:

31 Aug-2 Sep 1998