Sound of Guns: Digital Forensics of Gun Audio Samples meets Artificial Intelligence
This addresses a practical challenge in crime scene forensics and security by enabling gun identification from uncontrolled audio recordings, though it is incremental as it applies existing CNN methods to a new domain.
The paper tackles the problem of gun classification from audio samples without requiring controlled recording setups, achieving over 90% accuracy on a dataset of 3655 samples from YouTube videos.
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.