Independent Channel Residual Convolutional Network for Gunshot Detection
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Date
2022-05-01
Authors
Bajzík, Jakub
Přinosil, Jiří
Jarina, Roman
Mekyska, Jiří
0000-0003-3299-6204Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Science and Information Organization
Altmetrics
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.
Description
Citation
International Journal of Advanced Computer Science and Applications. 2022, vol. 13, issue 4, p. 950-958.
https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108
https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en