Detail publikace

Independent Channel Residual Convolutional Network for Gunshot Detection

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"
}