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Improving Mass Shooting Survivability: A Systematic Machine Learning Approach using Audio Classification and Source Localization

Authors

Jevon Mao1 and Marisabel Chang2, 1USA, 2California State Polytechnic University, USA

Abstract

Massshootings have emerged as a significant threat to public safety, with devastating consequences for communities and individuals affected by such events [7]. However, a lack of widespread use of new technological infrastructure poses significant risk to victims [8]. This paper proposes a system to classify and localize gunshots in reverberant indoor urban conditions, using MFCC features and a Convolutional Neural Network binary classifier [9]. The location information is further relayed to users through a mobile client in real time. We installed a prototype of the system in a high school in Orange County, California and conducted a qualitative evaluation of the approach. Preliminary results show that such a mass shooting response system can effectively improve survivability.

Keywords

Machine Learning, Public Safety, Acoustics, Directioning