Superimposed event detection by particle filters

Abstract

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.