Deciphering GunType Hierarchy through Acoustic Analysis of Gunshot Recordings
Ankit Shah, Rita Singh, Bhiksha Raj, Alexander Hauptmann

TL;DR
This study develops a machine learning-based acoustic analysis method to classify gunshot types from recordings, aiming for a cost-effective, real-time firearm detection system using common devices.
Contribution
It introduces a CNN-based framework for joint gunshot detection and firearm classification, demonstrating improved accuracy over traditional SVM methods.
Findings
Deep learning approach achieves 0.58 mAP on clean data.
SVM baseline achieves 0.39 mAP.
Performance drops to 0.35 mAP with noisy web data.
Abstract
The escalating rates of gun-related violence and mass shootings represent a significant threat to public safety. Timely and accurate information for law enforcement agencies is crucial in mitigating these incidents. Current commercial gunshot detection systems, while effective, often come with prohibitive costs. This research explores a cost-effective alternative by leveraging acoustic analysis of gunshot recordings, potentially obtainable from ubiquitous devices like cell phones, to not only detect gunshots but also classify the type of firearm used. This paper details a study on deciphering gun type hierarchies using a curated dataset of 3459 recordings. We investigate the fundamental acoustic characteristics of gunshots, including muzzle blasts and shockwaves, which vary based on firearm type, ammunition, and shooting direction. We propose and evaluate machine learning frameworks,…
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Taxonomy
TopicsSports Dynamics and Biomechanics · Gait Recognition and Analysis · Music Technology and Sound Studies
MethodsSupport Vector Machine
