Gunfire or Plastic Bag Popping? Trained Computer Can Tell the Difference

on the other hand, are trained to decipher information that is often irrelevant or imperceptible to human ears,” said Hanqi Zhuang, Ph.D., senior author, professor and chair, Department of Electrical Engineering and Computer Science, College of Engineering and Computer Science. “Similar to how bats swoop around objects as they transmit high-pitched sound waves that will bounce back to them at different time intervals, we used different environments to give the machine learning algorithm a better perception sense of the differentiation of the closely related sounds.”

For the study, gunshot-like sounds were recorded in locations where there was a likelihood of guns being fired, which included a total of eight indoor and outdoor locations. The data collection process started with experimentation of various types of bags, with trash can liners selected as the most suitable. Most of the audio clips were captured using six recording devices. To check on the extent of which a sound classification model could be confused by fake gunshots, researchers trained the model without exposing it to plastic bag pop sounds.

There were 374 gunshot samples initially used to train the model, which were obtained from the urban sound database. Researchers used 10 classes from the database (gun shot, dog barking, children playing, car horn, air conditioner, street music, siren, engine idling, jackhammer, and drilling). After training, the model was then used to test its ability to reject plastic bag pop sounds as true gunshot sounds.

“The high percentage of misclassification indicates that it is very difficult for a classification model to discern gunshot-like sounds such as those from plastic bag pop sounds, and real gunshot sounds,” said Rajesh Baliram Singh, first author and a Ph.D. student in FAU’s Department of Electrical Engineering and Computer Science. “This warrants the process of developing a dataset containing sounds that are similar to real gunshot sounds.” 

In gunshot detection, having a database of a particular sound that can be confused with gunshot sound yet is rich in diversity can lead to a more effective gunshot detection system. This concept motivated the researchers to create a database of plastic bag explosion sounds. The higher the diversity of the same sound the higher the likelihood that the machine learning algorithm will correctly detect that specific sound.

“Improving the performance of a gunshot detection algorithm, in particular, to reduce its false positive rate, will reduce the chances of treating innocuous audio trigger events as perilous audio events involving firearms,” said Stella Batalama, Ph.D., dean, College of Engineering and Computers Science. “This dataset developed by our researchers, along with the classification model they trained for gunshot and gunshot-like sounds is an important step leading to much fewer false positives and in improving overall public safety by deploying critical personnel only when necessary.”