# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 4 Results
### Gunshot Detection Deep Learning Model Results
Using the smartphone app designed for emergency responders, an emergency responder was also added to the system. \begin{table}
\begin{tabular}{l l} \hline \hline
**Hardware Part** & **Power** \\ \hline Jetson Nano’s CPU & 854 mW \\ \hline Jetson Nano’s GPU & 40 mW \\ \hline Entire Device & 2.7 W \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Power consumption of the gunshot detector device. Figure 9: (a) Photograph of the gunshot detector device: (1) Jetson Nano with a wireless module and cooling fan; (2) SD card; (3) antenna; (4) DC adapter; (5) power and reset switches; (6) sound card; (7) microphone; (8) heartbeat LED. (b) Gunshot detector device enclosed in casing. Figure 10: Screenshots of the smartphone apps: (**a**) emergency responder configuration in the emergency responder app; (**b**) user configuration and list of devices in the user app; (**c**) device properties configuration and buttons for its Wi-Fi setup and playing recorded gunshot sounds; (**d**) searching and connecting with the device using Bluetooth; (**e**) setup Wi-Fi SSID of the device. The gunshot detector system was tested inside a lab environment, by playing recorded sounds near the device rather, than performing actual shootings to avoid the destruction of properties. During testing, different sounds other than gunshots were played and they were successfully detected as others. Then, gunshot sounds were played, sometimes mixing with other environmental noise and background sounds, and the device successfully detected the sound as a gunshot and notified the central server within a second. Upon receiving the notification data from the device, the central server: successfully marked the location of the gunshot event on the map, displayed the assigned user and device information in the event log, saved the event data in the database, generated warning sounds, and sent smartphone notifications to the assigned user and all the emergency responders. Some screenshots of the central server software and smartphone app after a gunshot event are shown in Figures 11 and 12 namely. The system was also tested with multiple emergency responders, multiple users, and devices with many-to-many relationships, and notifications were sent successfully as expected. In the central server, gunshot events can be successfully searched using a range of dates and times, a rectangular area on the map, or both. A screenshot of the searching gunshot event is shown in Figure 13.  
Figure 11: Screenshot of the central server software marking gunshot event on map (**right**) and displaying associated information in the event log (**left**). Figure 12: Screenshots of the smartphone apps: (**a**) emergency responder app’s list view showing a list of gunshot events with location, date, and time; (**b**) clicking on the list item shows the direction to the gunshot event location on map; (**c**) user app’s list view showing a list of gunshot events with location, date, and time; (**d**) smartphone notification when gunshot is detected; (**e**) accessing recorded gunshot sounds on the device from user’s smartphone app.