Publication detail

Gunshot detection using convolutional neural networks

BAJZÍK, J. PŘINOSIL, J. KONIAR, D.

Original Title

Gunshot detection using convolutional neural networks

Type

conference paper

Language

English

Original Abstract

The main paper deals with the analysis of the methods of signal processing and events recognition in the audio signal and the implementation of the selected method in real use. Recognized events are gunshots mixed with a background sound such as traffic noise, human voice, animal sounds and other forms of environmental sounds. The proposed algorithm adapted for explosion detection can be used as part of a security system for monitoring depots or places dedicated to storing dangerous materials. For events classification and class recognition, the freely available machine learning frameworks TensorFlow and Keras are used.

Keywords

Acoustic signal processing, Gunshot detection systems, Image processing, Signal analysis.

Authors

BAJZÍK, J.; PŘINOSIL, J.; KONIAR, D.

Released

15. 6. 2020

Publisher

Institute of Electrical and Electronics Engineers Inc.

Location

Litva

ISBN

978-1-7281-5868-6

Book

24th International Conference Electronics, ELECTRONICS 2020

Pages from

1

Pages to

5

Pages count

5

URL

https://ieeexplore.ieee.org/document/9141621

BibTex

@inproceedings{BUT165911,
  author="Jakub {Bajzík} and Jiří {Přinosil} and Dušan {Koniar}",
  title="Gunshot detection using convolutional neural networks",
  booktitle="24th International Conference Electronics, ELECTRONICS 2020",
  year="2020",
  pages="1--5",
  publisher="Institute of Electrical and Electronics Engineers Inc.",
  address="Litva",
  doi="10.1109/IEEECONF49502.2020.9141621",
  isbn="978-1-7281-5868-6",
  url="https://ieeexplore.ieee.org/document/9141621"
}