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Detail publikace
BAJZÍK, J. PŘINOSIL, J. JARINA, R. MEKYSKA, J.
Originální název
Independent Channel Residual Convolutional Network for Gunshot Detection
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
Acoustic signal processing; gunshot detection systems; audio signal analysis; machine learning; deep learning; residual networks
Autoři
BAJZÍK, J.; PŘINOSIL, J.; JARINA, R.; MEKYSKA, J.
Vydáno
1. 5. 2022
Nakladatel
Science and Information Organization
ISSN
2156-5570
Periodikum
International Journal of Advanced Computer Science and Applications
Ročník
13
Číslo
4
Stát
Spojené království Velké Británie a Severního Irska
Strany od
950
Strany do
958
Strany počet
9
URL
https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108
Plný text v Digitální knihovně
http://hdl.handle.net/11012/209174
BibTex
@article{BUT180622, author="Jakub {Bajzík} and Jiří {Přinosil} and Roman {Jarina} and Jiří {Mekyska}", title="Independent Channel Residual Convolutional Network for Gunshot Detection", journal="International Journal of Advanced Computer Science and Applications", year="2022", volume="13", number="4", pages="950--958", doi="10.14569/IJACSA.2022.01304108", issn="2156-5570", url="https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108" }