Superimposed event detection by particle filters

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

Source separation  Event detection  Single-channel  Data handling  Rare event detection  Particle-filtering  source separatione  Electrical and Electronic Engineering  Jump Markov system  Background signals  Sound processing  Particle filters  Numerical experiments  Markov chain method  Parametric models  Particle filter  Signal detection  Bayesian estimation  Superimposed signal  Nonlinear filtering  Time domain analysis  Systems  Signal Processing  Markov processes  On-line detection  Discrete-time domain 

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.

Source: IET signal processing (Print) 5 (2011): 662–668. doi:10.1049/iet-spr.2010.0022

Publisher: IET,, Stevenage , Regno Unito

Metrics