An Adaptive Framework for Acoustic Monitoring of Potential Hazards

Abstract

Robust recognition of general audio events constitutes a topic of intensive research in the signal processing community. This work presents an efficient methodology for acoustic surveillance of atypical situations which can find use under different acoustic backgrounds. The primary goal is the continuous acoustic monitoring of a scene for potentially hazardous events in order to help an authorized officer to take the appropriate actions towards preventing human loss and/or property damage. A probabilistic hierarchical scheme is designed based on Gaussian mixture models and state-of-the-art sound parameters selected through extensive experimentation. A feature of the proposed system is its model adaptation loop that provides adaptability to different sound environments. We report extensive experimental results including installation in a real environment and operational detection rates for three days of function on a 24 hour basis. Moreover, we adopt a reliable testing procedure that demonstrates high detection rates as regards average recognition, miss probability, and false alarm rates.