CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Noise free Speech Enhancement based on Fast Adaptive Kalman Filtering Algorithm

by N. S. Banale, S. K. Sudhansu, S. M. Jagde
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 9
Year of Publication: 2014
Authors: N. S. Banale, S. K. Sudhansu, S. M. Jagde
10.5120/16247-5805

N. S. Banale, S. K. Sudhansu, S. M. Jagde . Noise free Speech Enhancement based on Fast Adaptive Kalman Filtering Algorithm. International Journal of Computer Applications. 93, 9 ( May 2014), 47-51. DOI=10.5120/16247-5805

@article{ 10.5120/16247-5805,
author = { N. S. Banale, S. K. Sudhansu, S. M. Jagde },
title = { Noise free Speech Enhancement based on Fast Adaptive Kalman Filtering Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 9 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 47-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number9/16247-5805/ },
doi = { 10.5120/16247-5805 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:24.659512+05:30
%A N. S. Banale
%A S. K. Sudhansu
%A S. M. Jagde
%T Noise free Speech Enhancement based on Fast Adaptive Kalman Filtering Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 9
%P 47-51
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Speech enhancement is the process of eliminating noise and increasing the quality of a speech signal, which is contaminated with other kinds of distortions. Conventional Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) model and perform a lot of matrix operations. In this paper we proposed a fast adaptive algorithm in presence of environment noise which eliminates the matrix operations and reduces the calculating time by only constantly updating the first value of state vector X(n). To evaluate the system performance we employed the calculation of SNR. Simulation results show that the fast adaptive algorithm using Kalman filtering is effective for speech enhancement.

References
  1. ZHANG Xiu-zhen, FU Xu-hui, WANG Xia, Improved Kalman filter method for speech enhancement. Computer Applications, Vol. 28, pp. 363-365, Dec. 2008.
  2. Quanshen Mai, Dongzhi He, Yibin Hou, Zhangqin Huang, "A Fast Adaptive Kalman Filtering Algorithm For Speech Enhancement", IEEE International Conference on Automation Science And Engineering, pp 327-332, 2011.
  3. WU Chun-ling, HAN Chong-zhao Square-Root Quadrature Filter. Acta Electronica Sinica, Vol. 37, No. 5, pp. 987-992, May. 2009.
  4. Marcel Gabrea, "Kalman Filter-Based Single Microphone Noise Canceller," International Workshop on Acoustic Echo and Noise Control, Sept. 2003, Kyoto.
  5. Adly A. Girgis, David G. Hart, Member, IEEE, "Implementation Of Kalman And Adaptive Kalman Filtering Algorithms For Digital Distance Protection On A Vector Signal Processor", IEEE Transactions on Power Delivery, Vol. 4, No. 1, pp 141-156, 1989.
  6. Fang Deng, Jie Chen, and Chen , " Adaptive unscented Kalman Filter for parameter and state estimate of nonlinear high speed objects", Journal of Systems Engineering and Electronics, Vol. 24, No. 4, 2013, pp. 655–665.
  7. Ali Almagbile, Jinling Wang, and Weidong Ding," Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration", Journal of Global Positioning Systems, Vol. 9, No. 1, 33-40, 2010.
  8. K. K. Paliwal and A. Basu, "A Speech Enhancement Method Based on Kalman Filtering", in Proc. ICASSP'87, pp. 177–180.
  9. Michel Verhaegen , Paul Van Dooren, Member, IEEE, "Numerical Aspects Of Different Kalman Filter Implementations", IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. AC-31. No. 10 ,pp 907-917, 1986.
  10. Maurice G. Bellanger, "Adaptive Digital Filters", Marcel Dekker, pp 416-419, 1987.
  11. Douglas, S. C. "Introduction to Adaptive Filters", Digital Signal Processing, CRC Press LLC, 1999.
  12. Angus P. Andrews, Mohinder S. Grewal , "Kalman Filtering Theory and Practice Using MATLAB" , John Wiley & Sons , pp 131-148, 2008
Index Terms

Computer Science
Information Sciences

Keywords

Kalman filter algorithm SNR (Signal to Noise Ratio) LPC.

Powered by PhDFocusTM