Unattended acoustic events classification at the vicinity of airports

This paper describes a method for the automatic identification of acoustic events using a weighted average of sound pressure and sound intensity measured at the vicinity of airports. The classification is based on the combination of different parameters using a technique conceptually similar to the sensor fusion: the indications of different classifiers are merged using the classification uncertainty as a figure of merit. The method uses the results of a training phase for the observation of statistical distributions of sound pressure and sound intensity related parameters. The different parameters’ weights are computed analyzing the overlap of probability distributions of takeoffs and landings, so that more relevance is given to the quantities presenting a low risk of misclassification. The proposed method does not require any arbitrary assumption about the parameter effectiveness, given that the indications of multiple (potentially infinite) classifiers can be merged together with weights that minimize the chance of misclassification. The method has been validated with measurements performed at the Milan Malpensa airport (Italy). Results outlined that the proposed classification criterion correctly identifies approximately 99% of events.