An acoustic surveillance system for critical event detection

In this paper, a novel acoustic surveillance system, that can be used in critical facilities and infrastructures, border security and public premises is introduced. As an application, an acoustic surveillance system that detects gunshots from recorded acoustic signals, and identifies the originating weapon type is selected. Mel Frequency Cepstral Coefficients, zero crossing rate, spectral percentile, spectral centroid, spectral spread and spectral flatness are used as features in different classifiers, namely, the Gaussian Mixture Models, k-Nearest Neighbor and Support Vector Machines, and their classification performances are compared. The feature space that is composed of features from four different classes is visually analyzed by using the Principle Component Analysis. For gun type identification, the best classification performance of 80% was achieved using the k-NN classifier.