Call for Paper
CAE solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 30, 2023.
On the Design and Performance Evaluation of DWT based Compressed Speech Transmission with Convolutional Coding
Javaid Ahmad Sheikh, Sakeena Akhtar, Sahir Majeed, Mehboob-ul-Amin and Shabir Ahmad Parah. Article: On the Design and Performance Evaluation of DWT based Compressed Speech Transmission with Convolutional Coding. Communications on Applied Electronics 4(9):36-40, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX
@article{key:article, author = {Javaid Ahmad Sheikh and Sakeena Akhtar and Sahir Majeed and Mehboob-ul-Amin and Shabir Ahmad Parah}, title = {Article: On the Design and Performance Evaluation of DWT based Compressed Speech Transmission with Convolutional Coding}, journal = {Communications on Applied Electronics}, year = {2016}, volume = {4}, number = {9}, pages = {36-40}, month = {April}, note = {Published by Foundation of Computer Science (FCS), NY, USA} }
Abstract
With the recent growth and advancement in multimedia based applications, many new techniques capable of producing good quality of compressed speech have been developed. Thus over the past few years there has been enough work done on compression and enhancement of speech signals. In this paper, the application of Discrete Wavelet Transform for speech compression along with the Convolutional Coding for error detection and correction has been described. A convolutionally encoded 8-DPSK modulated bit stream is transmitted through an AWGN channel. At the decoder the received binary bit stream is demodulated and decoded using Viterbi decoder. The compression is performed using Db10 Wavelets with Hard and Soft Thresholding algorithms. To evaluate the performance of the proposed technique, original and the reconstructed signal at the decoder are compared and various performance parameters in terms of Mean Square Error, Peak signal to Noise Ratio, Retained Signal Energy and Compression Ratio have been calculated. From the results obtained, it is observed that speech compression using Discrete Wavelet Transform along with Convolution Coding, shows better performance in terms of PSNR and MSE (high PSNR and low MSE) as compared to speech compression without convolution Coding. The proposed technique has a lot of scope in wireless communications where bandwidth and Quality of Service (QOS) are two main important factors that are taken into considerations.
References
- Rabiner L.R, and Schafer R.W. Digital Processing Of Speech Signal, Prentice Hall. 2012
- Amol. R. Madane, Zalak Shah, Raina Shah, Sanket Thakur. Speech Compression Using Linear predictive coding, Proceedings of the international Workshop on Machine Intelligence Research, 2009.
- Shiyo. M. Joseph, Firoz Shah A., Babu Auto. Comparing Speech Compression using Waveform coding and parametric coding, International Journal of Electronics Engineering, 3(1), 2011.
- Hemant Amhia and Ratish Kumar. A new approach of speech compression by using DWT and DCT, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.3, July 2014.
- Manas Arora, Neha Maurya, Poonam Pathak and Vartika Singh, “ speech compression analysis using matlab”, International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308, Volume: 03 Issue: 01 | Jan-2014.
- Sarika R. Gorantiwar and Naresh P. Jawarkar, “Speech Coding Techniques: A Review”, IJPRET, Volume 2 (8): 324-330, 2014.
- D. Ambika and V. Radha, “A Comparative Study between Discrete Wavelet Transform and Linear Predictive Coding”, World Congress on Information and Communication Technologies, 978-1-4673-4805-8/12/$31.00_c 2012 IEEE.
- Satish Kumar, O.P. Singh, G.R. Mishra, Saurab Kumar Mishra, Akanksha Trivedi, “Speech Compression and Enhancement using Wavelet Coders”, International Journal of Electronics Communication and Computer Engineering Volume 3, Issue 6, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209, 2012.
- Gilbert Strang and Truong Nguen, “Wavelets and Filter Banks”. Wellesley-Cambridge Press, MA, 1997, pp. 174-220,365-382
- Andrew K. Chan and Jaideva C. Goswami, “Fundamentals of Wavelets”, Wiley-India Edition, John Wiley & Sons Inc, New Delhi, 1999, pp. 89-97
- Nikhil Rao, “Speech Compression Using Wavelets”, ELEC 4801 THESIS PROJECT. School of Information Technology and Electrical Engineering, the University of Queensland, October 2001
- Brian Gamul kiewicz and Michael Weeks, “Wavelets based Speech Recognition”, Proc. IEEE International Symposium on Micro-Nano Mechatronics of Human Science,Dec.2003,pp. 678-681 Vol. 2,doi: 10.1109/MWSCAS.2003.1562377.
- Ingrid Daubechies, “Ten Lectures on Wavelets”, SIAM, 1992, pp. 115- 132,194-292,258-259
- T. Mc Dermott, “Wireless Digital Communications: Design and Theory”, Tuscon Amateur Packet Radio Corporation, Tuscon, Arizona, 1996.
- J. G. Proakis, “Digital Communications”, 3rd edition, WCB/McGraw-Hill, boston, Massachusetts, 1995.
- John. M. Geist, “Viterbi Decoder Performance in Gaussian Noise and Periodic Erasure Bursts ”IEEE Transactions on Communications, vol. com-28, no. 8, pp.1417-1422, August 1980.
- A.M. Michelson and A. H. Levesque, Error Control Techniques for Digital Communication, John Wiley & Sons, New York, 1985
Keywords
Speech Compression; Db10 Wavelet; Hard Thresholding; Soft Thresholding; Convolution Coding