Independent Channel Residual Convolutional Network for Gunshot Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146711" target="_blank" >RIV/00216305:26220/22:PU146711 - isvavai.cz</a>
Result on the web
<a href="https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108" target="_blank" >https://thesai.org/Publications/ViewPaper?Volume=13&Issue=4&Code=IJACSA&SerialNo=108</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14569/IJACSA.2022.01304108" target="_blank" >10.14569/IJACSA.2022.01304108</a>
Alternative languages
Result language
angličtina
Original language name
Independent Channel Residual Convolutional Network for Gunshot Detection
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Advanced Computer Science and Applications
ISSN
2156-5570
e-ISSN
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Volume of the periodical
13
Issue of the periodical within the volume
4
Country of publishing house
GB - UNITED KINGDOM
Number of pages
9
Pages from-to
950-958
UT code for WoS article
000798606400001
EID of the result in the Scopus database
2-s2.0-85130090786