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Design of Security System by Means of Sound Print

DOI: 10.4236/wjet.2018.64060, PP. 903-913

Keywords: Security System, Sound Signal, Spectrum Analyzer, Spectrogram

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Abstract:

Sound Recognition becomes an important tool for intrusion detection or for the monitoring of public premises exposed to personal hostility. It could further identify different sounds. The main idea of the sound recognition process in a security system is to store samples of different sound signals in the memory of the computer as references,?and?to be analyzed with respect to their frequencies components. In this paper, the sound signal of an unknown source would be analyzed and compared with all the available reference samples,?and?then recognition is made according to the closest sample. The developed security system consists of two main parts: the spectrum analyzer that converts the sound signal to spectrograms. It is designed based on the real-time analyzes, and the recognizer which compares the spectrograms and gives the decision of the recognition by using a special criterion. Experimental results prove that the accuracy of the proposed system can be 98.33% for the selected sample of signals.

References

[1]  Gaikwad, S.K., Gawali, B.W. and Yannawar, P. (2010) A Review on Speech Recognition Technique. International Journal of Computer Applications, 10, 16-24.
[2]  Juang, B.H. and Rabiner, L.R. (2005) Automatic Speech Recognition—A Brief History of the Technology Development. 2nd Edition, Elsevier, Amsterdam.
[3]  Martonow, M. (2009) Design of Fusion Classifiers for Voice-Based Access Control System of Building Security. WRI World Congress on Computer Science and Information Engineering, Los Angels, CA, 31 March-2 April 2009, 80-84.
[4]  Dixit, S. and Mulge, M.Y. (2014) Security System in Speech Recognition. International Journal of Computer Science and Mobile Computing (IJCSMC), 3, 275-284.
[5]  Jurafsky, D. and Martin, J.H. (2000) Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, Upper Saddle River, NJ.
[6]  Rabiner, L.R. and Juang B.H. (2004) Statistical Method for the Recognition and Understanding of Speech. Rutgers University and the University of California, Santa Barbara; Georgia Institute of Technology, Atlanta.
[7]  Shah, H.N.M., Ab Rashid, M.Z., Abdollah, M.F., Kamarudin, M.N., Lin, C.K. and Kamis, Z. (2014) Biometric Voice Recognition in Security System. Indian journal of Science and Technology, 7,104-112.
[8]  Poornima, S. (2016) Basic Characteristics of Speech Signal Analysis. International Journal of Innovative Research & Development, 5, 169-173.
http://www.internationaljournalcorner.com/index.php/ijird_ojs
[9]  (1997) Hints for Making Better Spectrum Analyzer Measurements. Application Note 1286-1, Hewlett-Packard.
[10]  Keysight Technologies Spectrum Analysis Basics. Application Note 150. HYPERLINK.
http://materias.fi.uba.ar/6644/info/anespec/basico/AN%20150%202016.pdf
[11]  Achankunju, S. and Mondikathi, Ch. (2015) Voice & Speech Based Security System Using MATLAB. International Journal of Emerging Trends in Electrical and Electronics, 11.
[12]  Nyquist-Shannon Sampling Theorem, Wikipedia, the Free Encyclopedia.

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