# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 5 Discussion and Future Work  
The proposed gunshot detection system is targeted for indoor use such as inside schools, grocery stores, and offices. Thus, possible false alarm sounds from outside, such as fireworks, may have less effect on this system. As discussed in Section 3.1.1, the other (i.e., the non-gunshot) dataset class contains possible false alarm sounds for gunshots such as fireworks, can opening, door knock, glass breaking, clapping, drum, and thunderstorms [25, 26, 27, 28, 29, 30, 31, 32]. Thus, the deep learning model is already trained to classify these sounds as other sounds. To further protect the system from false alarms generated by fireworks, the probability threshold level of the classifier can be automatically increased when fireworks generally happen--for instance, on 4 July and the night of 31 December in the USA. The proposed gunshot detection system can also be used in homes. However, the gunshot sounds generated by the people in the home from mobile, computer games, or movies can produce false alarms. To solve this problem, an app can be developed that will be installed on those mobiles and computers. The app will run in the background, read the sounds generated by the mobile or the computer, classify it as gunshot or other according to the proposed deep learning model, and then notify the gunshot detector device using Wi-Fi if a gunshot sound is detected in the mobile or in the computer. If the gunshot detector device detects a gunshot sound through its microphone and it also receives such notification from this app, it will then recognize the gunshot as a false alarm. We plan to implement this solution in the future. A mischievous act could be that someone intentionally plays a gunshot sound using a mobile device near the detector to create a false alarm. This mischievous act can be encountered by interfacing an infrared sensor with the gunshot detector device. Infrared radiation, which includes wavelengths beyond the visible spectrum, is emitted when a gunshot is fired [74]. This phenomenon is primarily associated with the intense heat generated during the firing process. When a firearm is discharged, the rapid combustion of  
Figure 13: Screenshot of central server software for searching gunshot events based on a chosen rectangular area as shown in red, date and time, or both. gunpowder within the cartridge produces extremely high temperatures. This intense heat causes the surrounding air and the firearm's components, including the barrel, to heat up significantly. As a result, these hot objects emit infrared radiation, which can be detected by infrared sensors. Thus, if the gunshot detector device detects only sound without any significant increase in infrared radiation near it, then it will be considered a false alarm. We plan to implement a sensor fusion approach by interfacing an infrared sensor with the device in the future. To further increase accuracy and lower false alarms, we plan to implement object detection (such as guns and people) from camera images [75]. Moreover, the proposed sound classification method can be used to detect situations such as crying, glass breaking, and drone arrivals [76]. Data transmission from the device to the central server using sockets may introduce security vulnerabilities. The issue of handling security is planned to be implemented in future work. The detected gunshot sounds, saved as WAV files inside the device, are not transmitted to the central server. These files can only be accessed by the user's smartphone when he/she is on the same Wi-Fi network as the device. Thus, there is no privacy concern. Due to safety reasons, the proposed gunshot detection system was tested by playing recorded gunshot sounds instead of actual shooting with firearms. We plan to test the system with actual guns inside a shooting range in the future. Moreover, if a silencer is used on a gun, then the generated sound will be different than the traditional gunshot sound.