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

Smailov N. Dosbayev Z. Omarov N. Sadykova B. Zhekambayeva M. Zhamangarin D. Ayapbergenova A.
2023 Science and Information Organization

International Journal of Advanced Computer Science and Applications
2023 #14 Issue 4 271 - 280 pp.

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

artificial intelligence , CNN , dangerous sound , deep learning , impulsive sound , RNN

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Satbayev University, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Kazakh University of Technology and Business, Astana, Kazakhstan

Satbayev University
Al-Farabi Kazakh National University
Kazakh University of Technology and Business

10 лет помогаем публиковать статьи Международный издатель

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