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Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold

by Rajeev Aggarwal, Jai Karan Singh, Vijay Kumar Gupta, Sanjay Rathore, Mukesh Tiwari, Anubhuti Khare
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 20 - Number 5
Year of Publication: 2011
Authors: Rajeev Aggarwal, Jai Karan Singh, Vijay Kumar Gupta, Sanjay Rathore, Mukesh Tiwari, Anubhuti Khare
10.5120/2431-3269

Rajeev Aggarwal, Jai Karan Singh, Vijay Kumar Gupta, Sanjay Rathore, Mukesh Tiwari, Anubhuti Khare . Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold. International Journal of Computer Applications. 20, 5 ( April 2011), 14-19. DOI=10.5120/2431-3269

@article{ 10.5120/2431-3269,
author = { Rajeev Aggarwal, Jai Karan Singh, Vijay Kumar Gupta, Sanjay Rathore, Mukesh Tiwari, Anubhuti Khare },
title = { Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 20 },
number = { 5 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume20/number5/2431-3269/ },
doi = { 10.5120/2431-3269 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:58.640745+05:30
%A Rajeev Aggarwal
%A Jai Karan Singh
%A Vijay Kumar Gupta
%A Sanjay Rathore
%A Mukesh Tiwari
%A Anubhuti Khare
%T Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold
%J International Journal of Computer Applications
%@ 0975-8887
%V 20
%N 5
%P 14-19
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, Discrete-wavelet transform (DWT) based algorithm are used for speech signal denoising. Here both hard and soft thresholding are used for denoising. Analysis is done on noisy speech signal corrupted by babble noise at 0dB, 5dB, 10dB and 15dB SNR levels. Simulation & results are performed in MATLAB 7.10.0 (R2010a). Output SNR (Signal to Noise Ratio) and MSE (Mean Square Error) is calculated & compared using both types of thresholding methods. Soft thresholding method performs better than hard thresholding at all input SNR levels. Hard thresholding shows a maximum of 21.79 dB improvement whereas soft thresholding shows a maximum of 35.16 dB improvement in output SNR.

References
  1. Prof. Dr. Ir. M. Steinbuch, Dr. Ir. M.J.G. van de Molengraft, June 7 (2005), Eindhoven University of Technology, Control Systems Technology Group Eindhoven, “Wavelet Theory and Applications”, a literature study, R.J.E. Merry, DCT 2005.53.
  2. MATKO SARIC, LUKI BILICIC, HRVOJE DUJMIC (2005), “White Noise Reduction of Audio Signal using Wavelets Transform with Modified Universal Threshold”, University of Split, R. Boskovica b. b HR 21000 Split, CROATIA.
  3. Adhemar Bultheel, September 22 (2003), “Wavelets with applications in signal and image processing”.
  4. D.L. Donoho, May (1992), Stanford University: "De-noising by Soft Thresholding”, IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 41, NO. 3.
  5. Alexandru Isar, Dorina Isar, May (2003) : “Adaptive denoising of low SNR signals”, Third International Conference on WAA 2003, Chongqing, P. R. China, 29-31, p.p. 821-826.
  6. Yuan Yan Tang, 2001, “Wavelet analysis and its applications”: second international conference, Springer-Verlag,(U.K.)
Index Terms

Computer Science
Information Sciences

Keywords

Discrete wavelet transform hard thresholding soft thresholding signal to noise ratio

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