  Classifying a weapon based on its muzzle blast is a challenging task that has
significant applications in various security and military fields. Most of the
existing works rely on ad-hoc deployment of spatially diverse microphone
sensors to capture multiple replicas of the same gunshot, which enables
accurate detection and identification of the acoustic source. However,
carefully controlled setups are difficult to obtain in scenarios such as crime
scene forensics, making the aforementioned techniques inapplicable and
impractical. We introduce a novel technique that requires zero knowledge about
the recording setup and is completely agnostic to the relative positions of
both the microphone and shooter. Our solution can identify the category,
caliber, and model of the gun, reaching over 90% accuracy on a dataset composed
of 3655 samples that are extracted from YouTube videos. Our results demonstrate
the effectiveness and efficiency of applying Convolutional Neural Network (CNN)
in gunshot classification eliminating the need for an ad-hoc setup while
significantly improving the classification performance.
