Gunshot Classification from Single-channel Audio Recordings using a Divide and Conquer Approach

Gunshot acoustic analysis is a field with many practical applications, but due to the multitude of factors involved in the generation of the acoustic signature of firearms, it is not a trivial task, especially since the recorded waveforms show a strong dependence on the shooter’s position and orientation, even when firing the same weapon. In this paper we address acoustic weapon classification using pattern recognition techniques with single channel recordings while taking into account the spatial aspect of the problem, so departing from the typical approach. We are working with three broad categories: rifles, handguns and shotguns. Our approach is based on two proposals: a Divide and Conquer classification strategy and the inclusion of some novel features based on the physical model of gunshot acoustics. The Divide and Conquer strategy is aimed at improving the rate of success of the classification stage by using previously retrieved spatial information to select between a set of specialized weapon classifiers. The minimum relative error reduction achieved when both proposals are used, compared with a single-stage classifier employing traditional features is 38.7%.