Volume 6, Issue 2 (February 2019), Pages: 33-38

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 Original Research Paper

 Title: Glass breaks detection system using deep auto-encoders with fuzzy rules induction algorithm

 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.2019.02.006

 Abstract:

Main uses of glass windows in commercial and residential buildings are prevalent. While a glass-based material has its advantages, it also poses security risks. Therefore, glass break detectors play an important role in security protection for offices and residential buildings. Conventional vibration-based and acoustic-based glass break detectors are designed to detect predetermined temporal and frequency feature thresholds of glass breakage sound signals. This leads to the inability to differentiate glass break from environmental sounds (such as the sound of striking objects, heavy sounds and shouted sounds) that are similar in their amplitude threshold and frequency pattern. Machine learning based acoustic audio classification has been popular in security surveillance applications. Researchers are interested in this research area, and different approaches have been proposed for anomaly event detection (such as gunshots, glass breakage sounds, etc.). This paper proposes a new design of a glass break detection algorithm based on Fuzzy Deep Auto-encoder Neural Network. The algorithm reduces false alarms and improves detection accuracy. Experimental results indicate that proposed fuzzy deep auto-encoder network system attained 95.5% correct detection for the proposed audio dataset. 

 © 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, Deep auto-encoder neural network, Fuzzy rule induction algorithm

 Article History: Received 25 July 2018,Received in revised form 27 November 2018, Accepted 5 December 2018

 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). Glass breaks detection system using deep auto-encoders with fuzzy rules induction algorithm. International Journal of Advanced and Applied Sciences, 6(2): 33-38

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 Figures

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 Tables

 Table 1 Table 2 

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