Volume 6, Issue 3 (March 2019), Pages: 56-61

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 Original Research Paper

 Title: Real time end-to-end glass break detection system using LSTM deep recurrent neural network

 Author(s): Wai Yan Nyein Naing *, Zaw Zaw Htike, Amir Akramin Shafie

 Affiliation(s):

 Mechatronic Engineering Department, International Islamic University Malaysia (IIUM), Gombak, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-5639-9899

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.03.009

 Abstract:

The aim of this paper is to propose a new design for a glass break detection system using LSTM deep recurrent neural networks at an end-to-end approach to reduce false positive alarm of state of the art glass break detectors. We utilized raw wave audio data to detect a glass break detection event in End-to-End learning approach. The key benefit of End-to-End learning is avoiding the need for hand-crafted audio features. To address the issue of a vanishing gradient and exploding gradient problem in conventional recurrent neural networks, this paper proposed deep long short term memory (LSTM) recurrent neural network to handle the sequence of the input audio data.  As a real-time detection result, the proposed glass break detection approach has a clear advantage over the conventional glass break detection system, as it yields significantly higher precision accuracy (99.999988 %) and suffers less from environmental noise that might cause a false alarm. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Glass break detection system, Deep learning, Long-short term memory, Deep recurrent neural network

 Article History: Received 15 May 2018, Received in revised form 12 January 2019, Accepted 18 January 2019

 Acknowledgement:

This work was supported by the Ministry of Higher Education Malaysia under PRGS17-002-0042 and International Islamic University Malaysia under RIGS16-350-0514. 

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Naing WYN, Htike ZZ, and Shafie AA (2019). Real time end-to-end glass break detection system using LSTM deep recurrent neural network. International Journal of Advanced and Applied Sciences, 6(3): 56-61

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 

 Tables

 Table 1

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