Gunfire or plastic bag popping? Trained computer can tell the difference
Engineering researchers develop gunshot detection algorithm and
classification model to discern similar sounds

Date:
December 13, 2021
Source:
Florida Atlantic University
Summary:
Engineering researchers have developed a gunshot detection algorithm
and classification model that can discern similar sounds such as
gunfire or a plastic bag popping. Discerning between a dangerous
audio event like a gun firing and a non-life-threatening event,
such as a plastic bag bursting, can mean the difference between
life and death. Additionally, it also can determine whether or
not to deploy public safety workers.

Humans, as well as computers, often confuse the sounds of a plastic
bag popping and real gunshot sounds.



FULL STORY ========================================================================== According to the Gun Violence Archive, there have been 296 mass shootings
in the United States this year. Sadly, 2021 is on pace to be America's deadliest year of gun violence in the last two decades.


========================================================================== Discerning between a dangerous audio event like a gun firing and
a non-life- threatening event, such as a plastic bag bursting, can
mean the difference between life and death. Additionally, it also can
determine whether or not to deploy public safety workers. Humans, as
well as computers, often confuse the sounds of a plastic bag popping
and real gunshot sounds.

Over the past few years, there has been a degree of hesitation over
the implementation of some of the well-known available acoustic gunshot detector systems since they can be costly and often unreliable.

In an experimental study, researchers from Florida Atlantic University's College of Engineering and Computer Study focused on addressing the
reliability of these detection systems as it relates to the false positive rate. The ability of a model to correctly discern sounds, even in the
subtlest of scenarios, will differentiate a well-trained model from one
that is not very efficient.

With the daunting task of accounting for all sounds that are similar
to a gunshot sound, the researchers created a new dataset comprised of
audio recordings of plastic bag explosions collected over a variety of environments and conditions, such as plastic bag size and distance from
the recording microphones. Recordings from the audio clips ranged from
400 to 600 milliseconds in duration.

Researchers also developed a classification algorithm based on a
convolutional neural network (CNN), as a baseline, to illustrate the
relevance of this data collection effort. The data was then used,
together with a gunshot sound dataset, to train a classification model
based on a CNN to differentiate life- threatening gunshot events from non-life-threatening plastic bag explosion events.



========================================================================== Results of the study, published in the journal Sensors,demonstrate
how fake gunshot sounds can easily confuse a gunshot sound detection
system. Seventy- five percent of the plastic bag pop sounds were
misclassified as gunshot sounds. The deep learning-based classification
model trained with a popular urban sound dataset containing gunshot sounds could not distinguish plastic bag pop sounds from gunshot sounds. However,
once the plastic bag pop sounds were injected into model training,
researchers discovered that the CNN classification model performed well
in distinguishing actual gunshot sounds from plastic bag sounds.

"As humans, we use additional sensory inputs and past experiences
to identify sounds. Computers, 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." Study co-author is Jeet Kiran Pawani,
M.S., who conducted the study while at Georgia Tech.

========================================================================== Story Source: Materials provided by Florida_Atlantic_University. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Rajesh Baliram Singh, Hanqi Zhuang, Jeet Kiran Pawani. Data
Collection,
Modeling, and Classification for Gunshot and Gunshot-like
Audio Events: A Case Study. Sensors, 2021; 21 (21): 7320 DOI:
10.3390/s21217320 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/12/211213121355.htm

--- up 1 week, 2 days, 7 hours, 13 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)