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ISSN: 2168-9679

Journal of Applied & Computational Mathematics
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Research Article

Human Voice Activity Detection using Wavelet

Md. Shahadat Hossain1* and Md. RafiqulIslam2

1B.Sc (Honors), Mathematics Discipline, Khulna University, Khulna, 9208, Bangladesh

2Mathematics Discipline, Khulna University, Khulna, 9208, Bangladesh

*Corresponding Author:
Md. Shahadat Hossain
Mathematics Discipline
Khulna University, Khulna
9208, Bangladesh
E-mail: [email protected]

Received Date: April 06, 2014; Accepted Date: April 17, 2015; Published Date: May 07, 2015

Citation: Hossain S, RafiqulIslam (2015) Human Voice Activity Detection using Wavelet. J Appl Computat Math 4: 216. doi: 10.4172/2168-9679.1000216

Copyright: © 2015 Hossain S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Wavelet has wide range of use in the present scientific universe. At present using wavelet through MATLAB different types of tasks are done. For instance biometric recognition (fingerprint recognition, voice recognition, iris recognition, face recognition, pattern recognition and signature recognition), signal processing, human voice activity detection etc. are done using wavelet and wavelet transform. Among these here I have discussed about “Human Voice Activity Detection”. At first a human voice is taken as the input sound to MATLAB command window using a good headphone for a few second. Then the sound taken as input give a graphical representation that is saved for future activities. After that using the wavelet toolbox of MATLAB the image of the input sound is taken for analyzing it. Using discrete wavelet transform the image is analyzed. During this analysis a “10 level wavelet” tree is generated by Haar wavelet with 10 decomposition level. At the same time the original signal is reconstructed. At the first time six different human voice activities of the same persons are analyzed. The Norm and the SNR (Signal to Noise Ratio) are counted. The data of the SNR are counted in decibel (db.) unit. Also the bit rates of the three different voice are counted. In this way total 18 different experiments are done for the different five persons where except the first person for all the person three experiments are dine.. The numerical data of the experiments are shown as graphical representation as well as in histogram analysis. In this process the whole experiments are done for the activity detection of human voice.

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