Research Article
Feature Extraction from Non-Audible Murmur (NAM) for the Vocally Handicapped using Wavelet Transform
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 135 - Issue 6 |
| Published: February 2016 |
| Authors: Sheena Christabel Pravin, Samyuktha Sundar, Krithika Aravindan |
10.5120/ijca2016908388
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Sheena Christabel Pravin, Samyuktha Sundar, Krithika Aravindan . Feature Extraction from Non-Audible Murmur (NAM) for the Vocally Handicapped using Wavelet Transform. International Journal of Computer Applications. 135, 6 (February 2016), 29-32. DOI=10.5120/ijca2016908388
@article{ 10.5120/ijca2016908388,
author = { Sheena Christabel Pravin,Samyuktha Sundar,Krithika Aravindan },
title = { Feature Extraction from Non-Audible Murmur (NAM) for the Vocally Handicapped using Wavelet Transform },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 135 },
number = { 6 },
pages = { 29-32 },
doi = { 10.5120/ijca2016908388 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A Sheena Christabel Pravin
%A Samyuktha Sundar
%A Krithika Aravindan
%T Feature Extraction from Non-Audible Murmur (NAM) for the Vocally Handicapped using Wavelet Transform%T
%J International Journal of Computer Applications
%V 135
%N 6
%P 29-32
%R 10.5120/ijca2016908388
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Non audible murmur is a body conducted silent speech through which the vocally handicapped can communicate. We propose a method of acquisition of Non Audible Murmur (NAM), (i.e., inaudible speech produced without vibrations of the vocal folds) from the vocally handicapped using the MEMS accelerometer, followed by its de-noising and Statistical Feature Extraction. The murmur is acquired by placing the sensor bonded to the surface of the skin over the soft-cartilage bone behind the ear. The resulting electrical signal is de-noised using Discrete Wavelet Transform (DWT). Statistical Features are extracted from the detailed co-efficients of the de-noised murmur.
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