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Speech Compression Using Wavelet Packet Best Tree Encoding (BTE)

Amr M. Gody, Safey A. Abdelwahab, Tamer M. Barakat, Mohamed Y. Mohamed

Abstract


Speech compression is one area of digital signal processing that focusing on reducing the bit rate of the speech signal for transmission or storage without significant loss of quality. This paper presents a new design feature for speech Compression using Wavelet Packet Transform and Linear prediction Coding. The proposed algorithm uses best tree decomposition of wavelet packets, which develop features afterward compressed signal is compressed by Linear prediction Coding. The 4 points encoded vector is a full of information just like the original best tree’s structure. The implied scoring system makes BTE suitable for compression problems and we reach at 12 compression ratio. The performance of speech signal is measure on the basis of signal to noise ratio (SNR), mean square error (MSE) and peak signal to noise ratio (PSNR).

Keywords


Speech Compression; Wavelet Packet Transform; BTE; LPC

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References


S. M. Joseph, “Spoken digit compression using wavelet packet,” International conference on signal and image processing, PP: 255-259, 2010.

M. A. Osman and N. Al, “Speech compression using LPC and wavelet,” 2nd International conference on computer engineering and technology, PP: V7-92 – V7-99, 2010.

R. W. Yeung, “A First Course in Information Theory,” New York: Kluwer Academic/Plenum Publishers, 2002.

J. Karam, “End point detection for wavelet based speech compression,” International journal of biological and life sciences, PP. 167-170, 2008.

R.V. Cox and P. Kroon, “Low bit-rate speech coders for multimedia communication”, IEEE Communications Magazine, pages 34-40, 1996. http://www.bell-labs.com

MatLab,http://www.mathworks.com/access/helpdesk/help/toolbox/wavelet/ch06_a11.html.

Gilbert Strang, Wavelets and filter banks, Wellesley-Cambridge Press, ISBN: 0-9614088-7-1, pp. 37-86, ©1996.

“A Tutorial of the Wavelet Transform” by Chun-Lin, Liu in February 23,2010.

Coifman, R.R.; M.V. Wickerhauser (1992), "Entropy-based algorithms for best basis selection," IEEE Trans. on Inf. Theory, vol. 38, 2, pp. 713-718.

Hai Jiang, Meng Joo Er and Yang Gao , Feature Extraction Using Wavelet Packets Strategy, Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, Hawaii USA, December 2003

http://en.wikipedia.org/wiki/Information_entropy.

Coifman, R.R.; M.V. Wickerhauser (1992), "Entropy-based algorithms for best basis selection," IEEE Trans. on Inf. Theory, vol. 38, 2, pp. 713 718.

Atal, B. S., & Hanauer, S. L. (1971). Speech analysis and synthesis by linear prediction of the speech wave. The Journal of the Acoustical Society of America, 50, 637–655.

Sroka, J. J., & Braida, L. D. (2005). Human and machine consonant recognition. Speech Communication, 45, 401–423.

Wu, J. D., & Lin, B. Fu. (2009). Speaker identification using discrete wavelet packet transform technique with irregular decomposition. Expert Systems with Applications, 36, 3136–3143.

Wu, J. D., & Ye, S. H. (2009). Driver identification based on voice signal using continuous wavelet transform and artificial neural network techniques. Expert Systems with Applications, 36, 1061–1069.

Perez-Meana, H. (2007). Advances in audio and speech signal processing: Technologies and applications. Hershey: IGI Global.

Edler, B., “Coding of Audio Signals with Overlapping Block Transform and Adaptive Window Functions,” (in German), Frequenz, vol.43, pp.252-256, 1989.

Q. Memon, T. Kasparis, “Transform Coding of Signals Using Approximate Trigonometric Expansions”. Journal of Electronic Imaging, Vol. 6, No. 4, October 1997, pp. 494-503.

Childers, D. G. (2000). Speech processing and synthesis toolboxes. New York: John Wiley & Sons.

Haydar, A., Demirekler, M., & Yurtseven, M. K. (1998). Speaker identification through use of features selected using genetic algorithm. Electronics Letters, 34, 39–40.

Lou, X., & Loparo, K. A. (2004). Bearing fault diagnosis on wavelet transform and fuzzy inference. Mechanical System and Signal Processing, 18, 1077–1095.

Lung, S. Y. (2006). Wavelet feature selection based neural networks with application to the text independent speaker identification. Pattern Recognition, 39, 1518–1521.


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