Non-stationary signal analysis using temporal clustering

Shai Policker, Amir B. Geva

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations


We present a model of non-stationary 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 non-stationary time series and a simple example of pre-processing a speech signal will be discussed. The model defines a non-stationary 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.

Original languageEnglish
Number of pages9
StatePublished - 1 Jan 1998
EventProceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII - Cambridge, Engl
Duration: 31 Aug 19982 Sep 1998


ConferenceProceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII
CityCambridge, Engl

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering


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