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

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.

Machine Learning, Public Safety, Acoustics, Directioning