Articolo in rivista, 2011, ENG, 10.1049/iet-spr.2010.0022

Superimposed event detection by particle filters

Urfalioglu O.; Kuruoglu E. E.; Cetin E. A.

Bilkent University, Ankara, Turkey; CNR-ISTI, Pisa, Italy

In this study, the authors consider online detection and separation of superimposed events by applying particle filtering. They observe only a single-channel superimposed signal, which consists of a background signal and one or more event signals in the discrete-time domain. It is assumed that the signals are statistically independent and can be described by random processes with known parametric models. The activation and deactivation times of event signals are assumed to be unknown. This problem can be described as a jump Markov system (JMS) in which all signals are estimated simultaneously. In a JMS, states contain additional parameters to identify models. However, for superimposed event detection, the authors show that the underlying JMS-based particle-filtering method can be reduced to a standard Markov chain method without additional parameters. Numerical experiments using real-world sound processing data demonstrate the effectiveness of their approach.

IET signal processing (Print) 5 (7), pp. 662–668

Keywords

Rare event detection, Particle filters, Bayesian estimation, source separatione, Systems

CNR authors

Kuruoglu Ercan Engin

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 199680

Year: 2011

Type: Articolo in rivista

Creation: 2013-01-22 12:11:33.000

Last update: 2017-10-13 19:32:42.000

External links

OAI-PMH: Dublin Core

OAI-PMH: Mods

OAI-PMH: RDF

DOI: 10.1049/iet-spr.2010.0022

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:199680

DOI: 10.1049/iet-spr.2010.0022

ISI Web of Science (WOS): 000296725800005

Scopus: 2-s2.0-80455167925