Document - Framework for online superimposed event detection by sequential Monte Carlo methods

2008

Conference article Open Access

Framework for online superimposed event detection by sequential Monte Carlo methods

Urfalioglu O., Kuruoglu E. E., Cetin E.

Bayesian estimation Event detection Importace sampling Acoustics Conditional density Signal filtering and prediction Monte Carlo methods Bayesian statistics Signal processing Sequential Monte Carlo Speech Mathematical models Argon SIR State space methods

In this paper, we consider online seperation and detection of superimposed events by applying particle filtering. We concentrate on a model where a background process, represented by a 1D-signal, is superimposed by an Auto-Regressive (AR) 'event signal', but the proposed approach is applicable in a more general setting. The activation and deactivation times of the event-signal are assumed to be unknown. We solve the online detection problem of this superpositional event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach.

Source: IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP, pp. 2125–2128, Las Vegas, USA, March 31 - April 4 2008

Publisher: IEEE, New York, USA

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@inproceedings{oai:it.cnr:prodotti:91851,
	title = {Framework for online superimposed event detection by sequential Monte Carlo methods},
	author = {Urfalioglu O. and Kuruoglu E.  E. and Cetin E.},
	publisher = {IEEE, New York, USA},
	doi = {10.1109/icassp.2008.4518062},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP, pp. 2125–2128, Las Vegas, USA, March 31 - April 4 2008},
	year = {2008}
}