Abstract
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 language | English |
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| Pages | 304-312 |
| Number of pages | 9 |
| State | Published - 1 Jan 1998 |
| Event | Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII - Cambridge, Engl Duration: 31 Aug 1998 → 2 Sep 1998 |
Conference
| Conference | Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII |
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| City | Cambridge, Engl |
| Period | 31/08/98 → 2/09/98 |
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering