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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Independent Channel Residual Convolutional Network for Gunshot Detection

Author 1: Jakub Bajzik
Author 2: Jiri Prinosil
Author 3: Roman Jarina
Author 4: Jiri Mekyska

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.01304108

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 4, 2022.

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Abstract: The main purpose of this work is to propose a robust approach for dangerous sound events detection (e.g. gunshots) to improve recent surveillance systems. Despite the fact that the detection and classification of different sound events has a long history in signal processing, the analysis of environmental sounds is still challenging. The most recent works aim to prefer the time-frequency 2-D representation of sound as input to feed convolutional neural networks. This paper includes an analysis of known architectures as well as a newly proposed Independent Channel Residual Convolutional Network architecture based on standard residual blocks. Our approach consists of processing three different types of features in the individual channels. The UrbanSound8k and the Free Firearm Sound Library audio datasets are used for training and testing data generation, achieving a 98 % F1 score. The model was also evaluated in the wild using manually annotated movie audio track, achieving a 44 % F1 score, which is not too high but still better than other state-of-the-art techniques.

Keywords: Acoustic signal processing; gunshot detection systems; audio signal analysis; machine learning; deep learning; residual networks

Jakub Bajzik, Jiri Prinosil, Roman Jarina and Jiri Mekyska, “Independent Channel Residual Convolutional Network for Gunshot Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01304108

@article{Bajzik2022,
title = {Independent Channel Residual Convolutional Network for Gunshot Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01304108},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01304108},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Jakub Bajzik and Jiri Prinosil and Roman Jarina and Jiri Mekyska}
}


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