Impulse Acoustic Event Detection, Classification, and Localization System

To point out dangerous events is more and more necessary not only in military applications but also in civil areas. In hazardous situations, to detect, localize, and classify a source of danger (e.g., gunshot), an automatic acoustic measurement detection system may be preferable in favor of visual detection. This article presents an automatic acoustic system that can detect, localize, and classify acoustic impulse events, such as gunshots. The presented system is based on standalone units, which continuously monitor its surrounding. If an acoustic event is detected, an algorithm based on Mel frequency transformation and classification using a support vector machine (SVM) is performed. If the gunshot is recognized, the system calculates, using the Levenberg-Marquardt iterative solution algorithm, the exact location of the event and can estimate the caliber of the used firearm. The detection system is designed to monitor the surroundings around objects of interest, like campuses, hospitals, or public areas. The system has been tested by 0.22, 6.35-mm, 7.65-mm, and 9-mm short guns, and 0.22, 5.56 NATO, and 7.62-mm rifle guns with various subsonic and supersonic ammunition. The sets of diverse impulse acoustic events, like different slams, glass breaking, dog barking, or similar, have been used to train the classifier to avoid false alarms. The presented acoustic system can reliably detect, localize and classify hazardous acoustic events.

Automated summary

Automatic acoustic measurement detection systems are increasingly necessary for pointing out dangerous events in both military and civil areas. This system detects, localizes, and classifies acoustic impulse events, such as gunshots, using Mel frequency transformation and a support vector machine (SVM) algorithm. It can accurately locate the event and estimate the caliber of the firearm used. Tested with various firearms and ammunition, this system can reliably detect, localize, and classify hazardous acoustic events by training the classifier with diverse impulse acoustic events.