CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

Distinctive Methods for Speech Enhancement using Kalman Filtering

by Chanchal Pandey, Sandeep Nemad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 5
Year of Publication: 2014
Authors: Chanchal Pandey, Sandeep Nemad
10.5120/18370-9511

Chanchal Pandey, Sandeep Nemad . Distinctive Methods for Speech Enhancement using Kalman Filtering. International Journal of Computer Applications. 105, 5 ( November 2014), 1-5. DOI=10.5120/18370-9511

@article{ 10.5120/18370-9511,
author = { Chanchal Pandey, Sandeep Nemad },
title = { Distinctive Methods for Speech Enhancement using Kalman Filtering },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 5 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number5/18370-9511/ },
doi = { 10.5120/18370-9511 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:36:53.301012+05:30
%A Chanchal Pandey
%A Sandeep Nemad
%T Distinctive Methods for Speech Enhancement using Kalman Filtering
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 5
%P 1-5
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In speech communication systems, it is mandatory to have noise free speech signal with high quality and clarity to obtain high performance. In real world it is very complicated to stockpile noise free speech signal all time for the speech communication system. It is found that speech signals get affected by background noise and tamper the system accuracy. It is very important to filter out the background noise form speech signal to enhance the performance of communication systems, it is also important to enhance the robustness of the speech code and also to enhance the listening ability. To filter out the background noise from the desired speech signal several speech filtering algorithms has been introduces in last few years. In this paper different speech enhancement systems have been examined and a Nobel method which is Second Ordered Fast Adaptive Extended Kalman Filter for speech enhancement has been proposed.

References
  1. Prasad, G. , and Surender. 2013 A Review of Different Approaches of Spectral Subtraction Algorithms for Speech Enhancement. Current Research in Engineering, Science and Technology (CREST) Journals. Vol. 01. Issue 02. 57-64.
  2. Ramalakshmi, K. 2013 speech enhancement with signal subspace filter based on perceptual post filtering. Dept of CSE Sri Ramakrishna Institute of Technology, Coimbatore. Vol. 2, Issue 1.
  3. Verteletskaya, E. , and Simak, B. 2011 Noise Reduction Based on Modified Spectral Subtraction Method. IAENG International Journal of Computer Science. Vol. 38.
  4. SUI, L. Y. , ZHANG, X. W. , HUANG, J. J. , and ZHOU, B. 2011 An Improved Spectral Subtraction Speech Enhancement Algorithm under Non-stationary Noise. Institute of Command Automation, PLAUST Nanjing, China.
  5. Abd El-Fattah, M. A. , Dessouky, M. I. , Diab, S. M. , and Abd El-samie, F. E. 2008 Speech Enhancement using an Adaptive Wiener Filtering approach. Department of Electronics and Electrical communications, Menoufia University, Menouf, Egypt, Progress In Electromagnetics Research M. Vol. 4. 167–184.
  6. Udrea, R. M. , Vizireanu, D. N. and Pirnog, I. 2007 A Perceptual Approach for Noise Reduction using Nonlinear Spectral Subtraction.
  7. Commins, B. 2005 Signal Subspace Speech Enhancement with Adaptive Noise Estimation. National University of Ireland, Galway.
  8. Kalman, R. E. 1900 A New Approach to Linear Filtering and Prediction Problems. ASME journal of basic engineering. Vol. 82. 35-45.
  9. Kalman, R. E. , and Buch, R. S. 1961 New Results in Linear Filtering and Prediction Theory. ASME journal of basic engineering. 95-108.
  10. Lee, Y. K. , Jung, G. W. , and Kwon, O. W. 2013 Speech Enhancement by Kalman Filtering with a Particle Filter-Based Preprocessor. IEEE International Conference on Consumer Electronics (ICCE). 340-341.
  11. Julier, S. J. , and Uhlmann, J. K. 2004 Unscented filtering and nonlinear estimation. Proceedings of the IEEE. 401–422.
  12. Hydari, M. 2009 Speech Signals Enhancement Using LPC Analysisbased on Inverse Fourier Methods. Department of Computer Engineering, Faculty of Engineering Noshirvani, Institute of Technology, Babol, Iran, Contemporary Engineering Sciences. Vol. 2.
  13. Goel, P. , and Garg, A. 2011 Review of spectral subtraction Technique for speech enhancement. IJECT. Vol. 2.
  14. Gao, L. , Guo, Y. , Li, S. , and Chen, F. 2009 Speech enhancement algorithm based on improved spectral subtraction. Intelligent Computing and Intelligent Systems. ICIS 2009. IEEE International Conference on, Shanghai. Vol. 3. 140-143.
  15. Fukane, A. R. 2011 Different Approaches of Spectral Subtraction method for Enhancing the Speech Signal in Noisy Environments. International Journal of Scientific & Engineering Research. Vol. 2. Issue 5.
  16. Ephraim, Y. , and Van Trees, H. L. 1995 A signal subspace approach for speech enhancement. IEEE Transactions on Speech and Audio Processing. Vol. 3. No. 4. 251–266. .
  17. Hermus, K. , Wambacq, P. , and Hamme, H. V. 2007 A Review of Signal Subspace Speech Enhancement and It's Application to Noise Robust Speech Recognition. Hindawi Publishing Corporation, EURASIP Journal on Advances in Signal Processing.
Index Terms

Computer Science
Information Sciences

Keywords

Speech Enhancement Speech Denoising Speech Communication Wiener Filtering Kalman Filter Fast Adaptive Kalman Filtering Second Ordered Fast Adaptive Extended Kalman Filter.