Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study

Distinguishing between a dangerous audio event like a gun firing and other non-life-threatening events, such as a plastic bag bursting, can mean the difference between life and death and, therefore, the necessary and unnecessary deployment of public safety personnel. Sounds generated by plastic bag...

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Autores principales:	Rajesh Baliram Singh, Hanqi Zhuang, Jeet Kiran Pawani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gunshot plastic bag pop binary classification convolution neural network Mel-frequency cepstral coefficients Mel-spectrogram Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/c1b80dd7b6524bf88b4eac063b162f97
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id	oai:doaj.org-article:c1b80dd7b6524bf88b4eac063b162f97
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spelling	oai:doaj.org-article:c1b80dd7b6524bf88b4eac063b162f972021-11-11T19:16:31ZData Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study10.3390/s212173201424-8220https://doaj.org/article/c1b80dd7b6524bf88b4eac063b162f972021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7320https://doaj.org/toc/1424-8220Distinguishing between a dangerous audio event like a gun firing and other non-life-threatening events, such as a plastic bag bursting, can mean the difference between life and death and, therefore, the necessary and unnecessary deployment of public safety personnel. Sounds generated by plastic bag explosions are often confused with real gunshot sounds, by either humans or computer algorithms. As a case study, the research reported in this paper offers insight into sounds of plastic bag explosions and gunshots. An experimental study in this research reveals that a deep learning-based classification model trained with a popular urban sound dataset containing gunshot sounds cannot distinguish plastic bag pop sounds from gunshot sounds. This study further shows that the same deep learning model, if trained with a dataset containing plastic pop sounds, can effectively detect the non-life-threatening sounds. For this purpose, first, a collection of plastic bag-popping sounds was recorded in different environments with varying parameters, such as plastic bag size and distance from the recording microphones. The audio clips’ duration ranged from 400 ms to 600 ms. This collection of data was then used, together with a gunshot sound dataset, to train a classification model based on a convolutional neural network (CNN) to differentiate life-threatening gunshot events from non-life-threatening plastic bag explosion events. A comparison between two feature extraction methods, the Mel-frequency cepstral coefficients (MFCC) and Mel-spectrograms, was also done. Experimental studies conducted in this research show that once the plastic bag pop sounds are injected into model training, the CNN classification model performs well in distinguishing actual gunshot sounds from plastic bag sounds.Rajesh Baliram SinghHanqi ZhuangJeet Kiran PawaniMDPI AGarticlegunshotplastic bag popbinary classificationconvolution neural networkMel-frequency cepstral coefficientsMel-spectrogramChemical technologyTP1-1185ENSensors, Vol 21, Iss 7320, p 7320 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gunshot plastic bag pop binary classification convolution neural network Mel-frequency cepstral coefficients Mel-spectrogram Chemical technology TP1-1185
spellingShingle	gunshot plastic bag pop binary classification convolution neural network Mel-frequency cepstral coefficients Mel-spectrogram Chemical technology TP1-1185 Rajesh Baliram Singh Hanqi Zhuang Jeet Kiran Pawani Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study
description	Distinguishing between a dangerous audio event like a gun firing and other non-life-threatening events, such as a plastic bag bursting, can mean the difference between life and death and, therefore, the necessary and unnecessary deployment of public safety personnel. Sounds generated by plastic bag explosions are often confused with real gunshot sounds, by either humans or computer algorithms. As a case study, the research reported in this paper offers insight into sounds of plastic bag explosions and gunshots. An experimental study in this research reveals that a deep learning-based classification model trained with a popular urban sound dataset containing gunshot sounds cannot distinguish plastic bag pop sounds from gunshot sounds. This study further shows that the same deep learning model, if trained with a dataset containing plastic pop sounds, can effectively detect the non-life-threatening sounds. For this purpose, first, a collection of plastic bag-popping sounds was recorded in different environments with varying parameters, such as plastic bag size and distance from the recording microphones. The audio clips’ duration ranged from 400 ms to 600 ms. This collection of data was then used, together with a gunshot sound dataset, to train a classification model based on a convolutional neural network (CNN) to differentiate life-threatening gunshot events from non-life-threatening plastic bag explosion events. A comparison between two feature extraction methods, the Mel-frequency cepstral coefficients (MFCC) and Mel-spectrograms, was also done. Experimental studies conducted in this research show that once the plastic bag pop sounds are injected into model training, the CNN classification model performs well in distinguishing actual gunshot sounds from plastic bag sounds.
format	article
author	Rajesh Baliram Singh Hanqi Zhuang Jeet Kiran Pawani
author_facet	Rajesh Baliram Singh Hanqi Zhuang Jeet Kiran Pawani
author_sort	Rajesh Baliram Singh
title	Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study
title_short	Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study
title_full	Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study
title_fullStr	Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study
title_full_unstemmed	Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study
title_sort	data collection, modeling, and classification for gunshot and gunshot-like audio events: a case study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c1b80dd7b6524bf88b4eac063b162f97
work_keys_str_mv	AT rajeshbaliramsingh datacollectionmodelingandclassificationforgunshotandgunshotlikeaudioeventsacasestudy AT hanqizhuang datacollectionmodelingandclassificationforgunshotandgunshotlikeaudioeventsacasestudy AT jeetkiranpawani datacollectionmodelingandclassificationforgunshotandgunshotlikeaudioeventsacasestudy
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Data Collection, Modeling, and Classification for Gunshot and Gunshot-like Audio Events: A Case Study

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