Biometrics and Bioinformatics

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Result Analysis to Compute the Entropy of Voice Signal and SNR Using MatLab


Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), India
2 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), India
3 MATS University, Raipur (C.G.), India
4 Multimedia Research Department, Multimedia Regional Center, Madhya Pradesh Bhoj (Open) University, Khandwa Road Campus, Indore (M.P.), India
5 Technocrats Institute of Technology, Bhopal (M.P.), India
     

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In this project report, (i.e. “Result Analysis to Compute Entropy of Voice Signal (CEVS) SNR using Matlab”) an approach is used to compute the entropy of given voice signal and signal to noise ratio (SNR) with the help of computed entropy. The main goals of this project are:
• To compute the tone of inputted voice signal
• To estimate entropy of tone
• To calculate SNR of entropy
To do this, psychoacoustic model and wavelet toolbox is used. Psychoacoustic model calculates masking threshold. Maximum distortion energy is computed from computed tone of inputted voice signal which defines the CEVS and SNR.


Keywords

Matlab 6.5, Wavelet Toolbox, Psychoacoustic Model, Algorithm.
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  • Result Analysis to Compute the Entropy of Voice Signal and SNR Using MatLab

Abstract Views: 214  |  PDF Views: 7

Authors

Shiv Kumar
Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), India
Vijay K. Chaudhari
Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), India
R. K. Singh
MATS University, Raipur (C.G.), India
Dinesh Varshney
Multimedia Research Department, Multimedia Regional Center, Madhya Pradesh Bhoj (Open) University, Khandwa Road Campus, Indore (M.P.), India
C. L. Saxena
Technocrats Institute of Technology, Bhopal (M.P.), India

Abstract


In this project report, (i.e. “Result Analysis to Compute Entropy of Voice Signal (CEVS) SNR using Matlab”) an approach is used to compute the entropy of given voice signal and signal to noise ratio (SNR) with the help of computed entropy. The main goals of this project are:
• To compute the tone of inputted voice signal
• To estimate entropy of tone
• To calculate SNR of entropy
To do this, psychoacoustic model and wavelet toolbox is used. Psychoacoustic model calculates masking threshold. Maximum distortion energy is computed from computed tone of inputted voice signal which defines the CEVS and SNR.


Keywords


Matlab 6.5, Wavelet Toolbox, Psychoacoustic Model, Algorithm.