International Journal of Speech Technology

, Volume 21, Issue 1, pp 65–77 | Cite as

A new method of speech transmission over space time block coded co-operative MIMO–OFDM networks using time and space diversity

  • Javaid A. SheikhEmail author
  • Sakeena Akhtar
  • Shabir A. Parah
  • G. M. Bhat


Reliable and good quality of service for speech transmission over wireless network has been a major challenge for the communication engineers and researchers. In this paper a new technique of speech compression and transmission using different Daubechies wavelets in a space time block coded co-corporative MIMO–OFDM networks using time and space diversity has been proposed. The main focus has been laid on design and development of wavelet based compression of multimedia signals for cooperative MIMO–OFDM system. We tried to find out various major issues regarding the wavelet compression of a speech signal. These issues include choice of a wavelet, decomposition level and thresholding criteria suitable for speech compression and transmission in co-operative MIMO–OFDM systems. A wavelet based speech compression technique using hard and soft thresholding algorithm has been proposed. The work shows that wavelet compression with QPSK modulation is a promising compression technique in a cooperative MIMO–OFDM system which makes use of the elegant theory of wavelets. The performance has been evaluated using mean square error, peak signal to noise ratio, compression ratio, bit error rate, and retained signal energy. It has been found that the transmitted speech signal is retrieved well under noisy conditions at higher order Daubechies wavelets. From the results it is clear that proposed technique aims at a radio access technology that can provide service performance comparable to that of current fixed Line accesses. To evaluate the performance of the proposed method, various performance parameters have been compared with previously implemented techniques and it has been found that the proposed work shows better performance as compared to the already existing techniques.


Cooperative communication MIMO–OFDM Speech compression Wavelet decomposition Soft thresholding Hard thresholding OSTBC 


  1. Ambika, D., & Radha, V. (2012). A comparative study between discrete wavelet transform and linear predictive coding. IEEE World Congress on Information and Communication Technologies, 978-1-4673-4805.Google Scholar
  2. Arora, M., Maurya, N., Pathak, P., & Singh, V. (2014). Speech compression analysis using matlab. International Journal of Research in Engineering and Technology, 03(01), eISSN: 2319-1163, ISSN: 2321-7308.Google Scholar
  3. Benyassine, A., Shlomot, E., & Su, H.-Y. (1997). ITU-T recommendation G.729 annex B: A silence compression scheme for use with G.729 optimized for V.70 digital simultaneous voice and data application. IEEE Communications, 35, 64–73.CrossRefGoogle Scholar
  4. Brian Gamul kiewicz and Michael Weeks. (2003). Wavelets based Speech Recognition. Proc. IEEE International Symposium on Micro-Nano Mechatronics of Human Science, 2, 678–681.
  5. Chisty, K. J. A., Islam, S. M. A., Ullah, S. E., & Sabuj, S. R. (2014). Scrambled voice frequency transmission in an amplify and forward relaying based STBC encode cooperative MIMO–OFDM system. International Journal of Signal Processing, Image Processing and Pattern Recognition, 7(2), 143–152.CrossRefGoogle Scholar
  6. Daubechies, I. (1992). Ten lectures on wavelets. SIAM, pp. 115–132,194–292,258–259.Google Scholar
  7. Gamul kiewicz, B. & Weeks, M. (2003). Wavelets based speech recognition. Proceedings of the IEEE International Symposium on Micro-Nano Mechatronics of Human Science (Vol. 2, pp. 678–681).
  8. Gaur, V. R., & Parikh, A. (2014). A spatial modulation based BER analysis of MIMO system under Rayleigh fading channel using STBC codes and complex wavelet transform. International Journal of Computer System, 01(02), ISSN: 2394-1065.Google Scholar
  9. Gorantiwar, S. R., & Jawarkar, N. P. (2014). Speech coding techniques: A review. IJPRET, 2(8), 324–330.Google Scholar
  10. Jankiraman, M. (2004). Space-time codes and MIMO systems. Boston: Artech House.Google Scholar
  11. Kumar, S., Singh, O. P., Mishra, G. R., Mishra, S. K., & Trivedi, A. (2012). Speech compression and enhancement using wavelet coders. International Journal of Electronics Communication and Computer Engineering, 3(6), ISSN (Online): 2249-2071X, ISSN (Print): 2278-4209.Google Scholar
  12. Manvendr, Jaiswal, A. K., Kumar, M., & Singh A. (2012). Voice synthesis using wavelet transform. International Journal of Scientific and Research Publications, 2(5), 483–489.Google Scholar
  13. Ramirez, J. (2007). Voice activity detection. Fundamentals and speech recognition system robustness, robust speech recognition and understanding. Vienna: I-Tech Education and Publishing.Google Scholar
  14. Rao, N. (2001). Speech compression using wavelets. ELEC 4801 THESIS PROJECT. School of Information Technology and Electrical Engineering, the University of Queensland.Google Scholar
  15. Rashed, M. D. G., Kabir, M. H., Reca, Md. S., Islam, Md. M., Shams, R. A., Masum, S., & Ullah, S. E. (2011). Transmission of voice signal: BER performance analysis of different FEC schemes based OFDM systems over various channels. International Journal of Advanced Science and Technology, 34, 89–100.Google Scholar
  16. Sheikh, J. A., Akhtar, S., Majeed, S., Mehboob-ul-Amin, & Parah, S. A. (2016a). On the design and performance evaluation of DWT based compressed speech transmission with convolutional coding. Communications on Applied Electronics, 4(9), 36–40. Scholar
  17. Sheikh, J. A., Akhter, S., Parah, S. A., & Bhat, G. M. (2016b). A new method of Haar and Db10 based secured compressed data transmission over GSM voice channels. Intelligent techniques in signal processing for multimedia security (Vol. 660, pp. 401–426). Studies in Computational Intelligence. Springer.$418.
  18. Sheikh, J. A., Akhtar, S., Parah, S. A., & Bhat, G. M. (2015). On the de-sign and performance evaluation of compressed speech transmission over wireless channel. 12th IEEE India International Conference (INDICON) on Electronics, Energy, Environment, Communication, Computers, Control (E3-C3) (pp. 17–20). New Delhi: Jamia Millia Islamia.Google Scholar
  19. Zayad, T., & Hanoon, A. (2005). Speech signal compression using wavelet and linear predictive coding. Al-Khwarizmi Engineering Journal, 1, 52–60.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Javaid A. Sheikh
    • 1
    Email author
  • Sakeena Akhtar
    • 1
  • Shabir A. Parah
    • 1
  • G. M. Bhat
    • 2
  1. 1.Department of Electronics & ITUniversity of KashmirSrinagarIndia
  2. 2.College of Engineering, ZakuraSrinagarIndia

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