2020/04/15 by Simone Raponi, Raponi, Simone, Isra Ali +3 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial intelligence #Audio and Speech Processing (eess.AS) #Computer science #Computer security #Computer vision #Convolutional neural network #Digital Media Forensic Detection #Digital forensics #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Identification (biology) #Machine Learning (cs.LG) #Microphone #Music and Audio Processing #Muzzle #Software deployment #Sound (cs.SD) #Task (project management) #Telecommunications #cs.LG #cs.SD #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.07948
openalex publication_date 2020/04/15 · arxiv created 2021/03/01 · arxiv updated 2021/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.