Gunshot noise detection using zero phase technique

Thanhikam, Weerawut

doi:10.1109/acdt.2015.7111608

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The work in ref. [9] proposes a gunshot noise detection method using Zero-Phase Technique. The works in ref.…”

Section: Related Workmentioning

confidence: 99%

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Towards an Indoor Gunshot Detection and Notification System Using Deep Learning

Khan

2023

ASI

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Gun violence and mass shootings kill and injure people, create psychological trauma, damage properties, and cause economic loss. The loss from gun violence can be reduced if we can detect the gunshot early and notify the police as soon as possible. In this project, a novel gunshot detector device is developed that automatically detects indoor gunshot sound and sends the gunshot location to the nearby police station in real time using the Internet. The users of the device and the emergency responders also receive smartphone notifications whenever the shooting happens. This will help the emergency responders to quickly arrive at the crime scene, thus the shooter can be caught, injured people can be taken to the hospital quickly, and lives can be saved. The gunshot detector is an electronic device that can be placed in schools, shopping malls, offices, etc. The device also records the gunshot sounds for post-crime scene analysis. A deep learning model, based on a convolutional neural network (CNN), is trained to classify the gunshot sound from other sounds with 98% accuracy. A prototype of the gunshot detector device, the central server for the emergency responder’s station, and smartphone apps have been developed and tested successfully.

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“…The work in ref. [9] proposes a gunshot noise detection method using Zero-Phase Technique. The works in ref.…”

Section: Related Workmentioning

confidence: 99%

“…The works in ref. [8,9] process the signals in the time domain instead of using state-of-the-art machine learning techniques. The accuracy of their approaches was not reported and no hardware implantation results are presented.…”

Section: Related Workmentioning

confidence: 99%