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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Author 1: Nurzhigit Smailov
Author 2: Zhandos Dosbayev
Author 3: Nurzhan Omarov
Author 4: Bibigul Sadykova
Author 5: Maigul Zhekambayeva
Author 6: Dusmat Zhamangarin
Author 7: Assem Ayapbergenova

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2023.0140431

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 4, 2023.

  • Abstract and Keywords
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Abstract: In this research paper, we presented a novel approach to detect impulsive sounds in real-time using a combination of Deep CNN and RNN architectures. The proposed approach was evaluated using our collected dataset of impulsive sounds, and the results showed that it outperformed traditional audio signal processing methods in terms of accuracy and F1-score. The proposed approach has several advantages over traditional methods, including the ability to handle complex audio patterns, detect impulsive sounds in real-time, and improve its performance with a large dataset of labeled impulsive sounds. However, there are some limitations to the proposed approach, including the requirement for a large amount of labeled data to train effectively, environmental factors that may impact the accuracy of the detection, and high computational requirements. Overall, the proposed approach demonstrates the effectiveness of using a combination of Deep CNN and RNN architectures for impulsive sound detection, with potential applications in various fields such as public safety, industrial settings, and home security systems. The proposed approach is a significant step towards developing automated systems for detecting dangerous events and improving public safety.

Keywords: CNN; RNN; deep learning; impulsive sound; dangerous sound; artificial intelligence

Nurzhigit Smailov, Zhandos Dosbayev, Nurzhan Omarov, Bibigul Sadykova, Maigul Zhekambayeva, Dusmat Zhamangarin and Assem Ayapbergenova, “A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140431

@article{Smailov2023,
title = {A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140431},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140431},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Nurzhigit Smailov and Zhandos Dosbayev and Nurzhan Omarov and Bibigul Sadykova and Maigul Zhekambayeva and Dusmat Zhamangarin and Assem Ayapbergenova}
}


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