List of Journals    /    Call For Papers    /    Subscriptions    /    Login
 
 
 
 
 SEARCH
By Author By Title
 
 
ABOUT CSC
 About CSC Journals
 CSC Journals Objectives
 List of Journals
 CALL FOR PAPERS
 Call For Papers CFP
 Special Issue CFP
AUTHOR GUIDELINES
 Submission Guidelines
 Peer Review Process
 Helpful Hints For Getting Published
 Plagiarism Policies
 Abstracting & Indexing
 Open Access Policy
 Submit Manuscript
 FOR REVIEWERS
 Reviewer Guidelines
 FOR EDITORIAL
 Editor Guidelines
 Join Us As Editor
 Launch Special Issue
 Suggest New Journal
 CSC LIBRARY
 Browse CSC Library
 Open Access Policy
  SERVICES
 Conference Partnership Program (CPP)
 Abstracting & Indexing
 SUBSCRIPTIONS
 Subscriptions
 Discounted Packages
 Archival Subscriptions
 How to Subscribe
 Librarians
 Subscriptions Agents
 Order Form
 DOWNLOADS
 
 
 
 
A New Approach for Speech Enhancement Based On Eigenvalue Spectral Subtraction
Full text
 PDF(151.5KB)
Source 
Signal Processing: An International Journal (SPIJ)
Table of Contents
Download Complete Issue    PDF(1.45MB)
Volume:  3    Issue:  4
Pages:  34-82
Publication Date:   August 2009
ISSN (Online): 1985-2339
Pages 
34 - 41
Author(s)  
 
Published Date   
 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Eigenvalues, singular values decomposition, Spectral Subtraction, Speech enhancement 
 
 
This Manuscript is indexed in the following databases/websites:-
1. Directory of Open Access Journals (DOAJ)
2. Docstoc
3. Scribd
4. PDFCAST
5. CiteSeerX
6. ScientificCommons
7. WorldCat
8. Google Scholar
9. ResearchGATE
10. Academic Index
11. Bielefeld Academic Search Engine (BASE)
12. refSeek
13. iSEEK
14. Socol@r
 
 
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria. 
 
 
 
1 S.F. Boll, “Suppression of acoustic noise in speech using spectral subtraction”. IEEE Transactions Acoustics Speech Signal Process. 27, 113:120, 1979.
2 Y. Ephraim, D. Malah, “Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator”. IEEE Transactions Acoust. Speech Signal Process. ASSP. 32 (6), 1109:1121. 1984.
3 D.L. Donoho, “Denoising by soft thresholding”. IEEE Transactions Information Theory, 41(3), 613:627, 1995.
4 Y. Ghanbari, M. R. Karami-M, “A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets”, Speech Communication 48, 927:940, 2006
5 S. Sayeed, N. S. Kamel and R. Besar. “A Sensor-Based Approach for Dynamic Signature Verification using Data Glove”. Signal Processing: An International Journal (SPIJ),2(1)1:10,2008
6 Guo, D., Zhu, W., Gao, Z., Zhang, J., 2000, A study of wavelet thresholding denoising. Paper presented at the International Conference on Signal Processing, Beijing, PR China.
7 Ghanbari Y., Karami-M M. R., 2006, A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets, Speech Communication 48 (2006) 927 940
8 Gustafsson, H., Nordholm, S. E., & Claesson, I., 2001, Spectral Subtraction Using Reduced Delay Convolution and Adaptive Averaging. IEEE Transactions on Speech and Audio Processing, 9(8), pp. 799-807.
9 Martin, R. (1994). Spectral Subtraction Based on Minimum Statistics. Paper presented at the Europe Signal Processing Conference, Edinburgh, Scotland, pp. 1182-1185.
10 Tsoukalas, D. E., Mourjopoulos, J. N., & Kokkinakis, G. (1997). Speech Enhancement Based on Audible Noise Suppression. IEEE Transactions on Speech and Audio Processing, 5(6), pp. 497 514.
11 Michael T. Johnson , Andrew C. Lindgren, Richard J. Povinelli, Xiaolong Yuan , 2003, Performance of Nonlinear Speech Enhancement Using Phase Space Recognition Struction, ICASSP 2003
12 Banbrook M., S. McLaughlin, and 1. Mann, 1999, Speech characterization and synthesis by nonlinear methods, IEEE Transactions on Speech and Audio Processing, vol. 7, pp. I -17.
13 Kumar A. and S. K. Mullick, 1996, Nonlinear Dynamical Analysis of Speech, Journal o/the Acoustical society of America, vol. 100, pp. 615-629.
14 Abarbanel H. D. I. 1996, Analysis of Observed Chaotic Data (Springer, New York, 1996).
15 Kantz H. and T. Schreiber, 1997, Nonlinear Time Series Analysis (Cambridge University Press, Cambridge, England,1997).
16 Takens F., 1981, In Dynamical Systems and Turbulence, edited by D. A. Rand and L.-S. Young, Lecture Notes, in Mathematics Vol. 898 (Springer-Verlag, New York, 1981), P. 366
17 Hegger R., H. Kantz, and L. Matassini, 2000, Denoising Human Speech Signals Using Chaoslike Features, Physical Review Letters (3 APRIL 2000), Vol. 84, Num. 14, PP. 3197-31200
18 H. Sameti, H. Sheichzadeh, Li Deng, R. L. Brennan, “HMM Based Strategies for Enhancement of Speech Signals Embedded in Nonstationary Noise”, IEEE Transactions on Speech and Audio processing, vol. 6, No. 5, September 1998.
 
 
 
 
 
 
 
 
Jamal Ghasemi : Colleagues
Karami mollaei : Colleagues  
 
 
 
  Untitled Document
 
Copyrights (c) 2012 Computer Science Journals. All rights reserved.
Best viewed at 1152 x 864 resolution. Microsoft Internet Explorer.
 
  
 
Copyrights & Usage: Articles published by CSC Journals are Open Access. Permission to copy and distribute any other content, images, animation and other parts of this website is prohibited. CSC Journals has the rights to take action against individual/group if they are found victim of copying these parts of the website.