Research Article
Combined Speech Compression and Encryption using Contourlet Transform and Compressive Sensing
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 140 - Issue 5 |
| Published: April 2016 |
| Authors: Maher K.M. Al-Azawie, Ali M. Gaze |
10.5120/ijca2016909295
|
Maher K.M. Al-Azawie, Ali M. Gaze . Combined Speech Compression and Encryption using Contourlet Transform and Compressive Sensing. International Journal of Computer Applications. 140, 5 (April 2016), 6-10. DOI=10.5120/ijca2016909295
@article{ 10.5120/ijca2016909295,
author = { Maher K.M. Al-Azawie,Ali M. Gaze },
title = { Combined Speech Compression and Encryption using Contourlet Transform and Compressive Sensing },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 140 },
number = { 5 },
pages = { 6-10 },
doi = { 10.5120/ijca2016909295 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A Maher K.M. Al-Azawie
%A Ali M. Gaze
%T Combined Speech Compression and Encryption using Contourlet Transform and Compressive Sensing%T
%J International Journal of Computer Applications
%V 140
%N 5
%P 6-10
%R 10.5120/ijca2016909295
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper introduces a new technique for Speech compression and encryption in one-step. Speech compression is the process of Converting human speech signals into a form that is compact and is reliable for communication and storage by reducing the size of data without losing quality of the original speech. Speech encryption is the process of converting the normal form of speech into unrecognized form to increase the security of communication through an insecure channel. Compressive sensing theory is used to apply the compression and encryption in one-step; in addition, the contourlet transform is used to prove the principle of Compressive Sensing (CS) (i.e. Spars structure) that is one of the most important aspect of the compressive sensing theory..
References
- Lawrence R Rabiner ‘Digital Processing of Speech Signals’ (2nd Ed),Pearson Education, 2005 ISBN 81-297-0272-X.
- Kalid Sayood ‘Introduction to Data Compression’ (2nd Ed), Morgan Kaufmann Publishers, 2005.ISBN 81-8147-191-1.
- S.Haykin, Communication Systems, (4th Edn) John Wiley & Sons, New York, 2001.ISBN 0-471-17869-1.
- Changgui Shi,Bharat Bhargara, (1998). "Fast MPEG Video Encryption Algorthim", Department of computer Sciences, Purdue University.
- E. J. Candes and M. B. Wakin, "An Introduction to Compressive Sampling," IEEE, Signal Processing Magazine, pp. 21-30, 2008.
- R. G. Baraniuk, "Compressive sensing," IEEE Signal Processing Magazine, vol. 24, pp. 118-121, 2007.
- T. V. Sreenivas and W. B. Kleijn, "Compressive sensing for sparsely excited speech signals," in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 4125-4128, 2009.
- D.L. Donoho, "Compressed Sensing," IEEE Transactions on Information Theory, vol. 52, pp.1289-1306, 2006.
- E. Candes, J. Romberg, and T. Tao, “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information,” IEEE Transaction on Information Theory, vol. 52, pp. 489–509, 2006.
- E. J. Candes and M. B. Wakin, "An Introduction to Compressive Sampling," IEEE, Signal Processing Magazine, pp. 21-30, 2008.
- J. A. Tropp and A. C. Gilbert, "Signal Recovery from Random Measurements via Orthogonal Matching Pursuit," IEEE Transactions on Information Theory, vol. 53, pp. 4655-4666, 2007.
- M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, "Gradient projection for sparse reconstruction," IEEE Journal of Selected Topics in Signal Processing, vol. 1, pp. 586-597, 2007.
- H. Rauhut, K. Schnass, and P. Bandergheynst, "Compressed sensing and redundant dictionaries," IEEE Transactions on Information Theory, vol. 54, pp. 2210-2219, 2008.
- N. Hurley and S. Rickard, "Comparing Measures of Sparsity," IEEE Transactions on Information Theory, vol. 55, pp. 4723-4741, 2009.
- Zanartu M., 2005,” Audio Compression using Wavelet Techniques”, University Purdue, Electrical and computer engineering.
- E.L. Pennec and S. Mallat. Image Compression with Geometric Wavelets. IEEE International Conference on Image Processing, 2000.
- M. Do. Directional Multiresolution Image Representations. Ph.D. Thesis, Department of Communication Systems, Swiss Federal Institute of Technology Lausanne, November 2001.
- P. J. Burt, E. H. Adelson. The Laplacian pyramid as a compact image coder. IEEE Trans. Commun., Vol.31 (4):532-540, April 1983
- M. Do and M. Vetterli. Framing Pyramids. IEEE Trans. On Signal Processing, VOL. 51, NO.9, September 2003.
- M. Do and M. Vetterli. Contourlets. In: J. Stoeckler, G. V. Welland (Eds.), Beyond Wavelets, pp.1-27., Academic Press, 2002.
- M. N. Do and M. Vetterli, “Contourlets,” in Beyond Wavelets, Academic Press, New York, 2003.
- Journal of Engineering and Development, Vol. 17, No.4, October 2013, ISSN 1813- 7822 Speech Scrambling Employing Lorenz Fractional Order Chaotic System.
- J. Benesty, M. Sondhi, Y. Huang (Ed.), Springer Handbook of Speech Processing. Berlin Heidelberg: Springer-Verlag, 2008.
- Y. Hu and P. Loizou, “Evaluation of objective quality measures for speech enhancement,” IEEE Transactions on Speech and Audio Processing, 16(1), pp. 229-238, 2008.
Index Terms
Keywords