Research Article

Feature Extraction from Non-Audible Murmur (NAM) for the Vocally Handicapped using Wavelet Transform

by  Sheena Christabel Pravin, Samyuktha Sundar, Krithika Aravindan
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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.

References
  • B. Denby, T. Schultz, K. Honda, T. Hueber, M.Gilbert, and S.Brumberg. Silent speech interfaces, Speech Communication, Vol. 52, No. 4, pp. 270-287, 2010.
  • Y. Nakajima, H. Kashioka, N. Cambell, and K. Shikano.”Non-Audible Murmur (NAM) recognition”, IEICE Trans. Information and Systems,Vol. E89-D, No. 1, pp. 1-8, 2006.
  • Ashish Khare and Uma Shanker Tiwary, “Soft-thresholding for denoising of medical images - a multiresolution approach”, International Journal of Wavelets, Multiresolution and Information Processing, 3(4):477–496, April 2005.
  • Jim Lambers,’Introductino to Wavelet Analysis’.
  • Duraisamy Sundararajan ,‘Fundamentals of the Discrete Haar Wavelet Transform’
  • Yoshitaka Nakajima, Hideki Kashioka, Kiyohiro Shikano. Nick Campbell,”Non-Audible Murmur Recognition Input Interface Using Stethoscopic Microphone Attached To the Skin”, Proceedings of ICASSP’03, April 2003.
  • Ishimitsu, Nakayama ,“Construction of Speech Support System Using body-conducted Speech recognition for disorders”, Proceedings of ICICIC, June 2008.
  • Heracleous, et.al., “Accurate Hsidden Markov models for non-audible murmur (NAM) recognition based on iterative supervised adaptation”, Automatic Speech Recognition and Understanding, ASRU '03,pp.73-76,Dec.2003.
  • Tomoki Toda,’ Statistical Approaches to Enhancement of Body-Conducted Speech Detected with Non-Audible Murmur Microphone’, Proceedings of 2012 ICME International Conference on Complex Medical Engineering July I - 4, Kobe, Japan,2012.
  • Shiwen Deng, Jiqing Han, Tieran Zheng, Guibin Zheng,”A modified MAP criterion based on Hidden Markov Model for Voice Activity Detection”, Proceedings of ICASSP-2011
  • Yan Yin, Qi Li,“Soft frame margin estimation of gaussian mixture models for speaker Recognition with sparse training data”, Proceedings of ICASSP-2011.
  • Tomaki Toda, Alan W.Black, Keiichi Tokuda, “Voice Conversion Based on Maximum Likelihood Estimation of Specrtal Parameter Trajectory”, Audio, Speech and Language Processing Journal, Vol.15, Issue:8,pp.2222-2235, Oct.2007.
  • http://www.analog.com/en/products/mems/mems-accelerometers/adxl335.html#product-overview
  • Mohammed A. Salem, Nivin Ghamry, and BeateMeffert, “Daubechies Versus Biorthogonal Wavelets for Moving Object Detection in Traffic Monitoring Systems”,2009
  • Ingrid Daubechies. Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, 1992.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

NAM MEMS accelerometer DWT De-noising Feature Extraction Vibration sensor.

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