Combined speech compression and encryption using chaotic compressive sensing with large key size

Combined speech compression and encryption using chaotic compressive sensing with large key size

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This study introduces a new method for speech signal encryption and compression in a single step. The combined compression/encryption procedures are accomplished using compressive sensing (CS). The contourlet transform is used to increase the sparsity of the signal required by CS. Due to its randomness properties and very high sensitivity to initial conditions, the chaotic system is used to generate the sensing matrix of CS. This largely increases the key size of encryption to when logistic map is used. A spectral segmental signal-to-noise ratio of −36.813 dB is obtained as a measure of encryption strength. The quality of reconstructed speech is given by means of signal-to-noise ratio (SNR), and perceptual evaluation speech quality (PESQ). For 60% compression ratio the proposed method gives 48.203 dB SNR and 4.437 PESQ for voiced speech segments. However, for continuous speech (voiced and unvoiced), it gives 41.097 dB SNR and 4.321 PESQ.


    1. 1)
      • 1. Haykin, S.: ‘Communication systems’ (John Wiley & Sons, New York, 2001, 4th edn.), ISBN 0-471-17869-1.
    2. 2)
      • 2. Baraniuk, R.G.: ‘Compressive sensing’, IEEE Signal Process. Mag., 2007, 24, pp. 118121.
    3. 3)
      • 3. Mahmood, M.K., Gaze, A.M.: ‘Combined speech compression and encryption using contourlet transform and compressive sensing’, Int. J. Comput. Appl., 2016, 140, (5), pp. 610 (ISSN:0975–8887).
    4. 4)
      • 4. Donoho, D.L.: ‘Compressed sensing’, IEEE Trans. Inf. Theory, 2006, 52, pp. 12891306.
    5. 5)
      • 5. Zeng, L., Zhang, X., Chen, L., et al: ‘Scrambling-based speech encryption via compressed sensing’, EURASIP J. Adv. Signal Process., 2012, 257, pp. 112.
    6. 6)
      • 6. Tropp, J.A., Gilbert, A.C.: ‘Signal recovery from random measurements via orthogonal matching pursuit’, IEEE Trans. Inf. Theory, 2007, 53, pp. 46554666.
    7. 7)
      • 7. Lorenz, E.N.: ‘Deterministic nonperodic flow’, J. Atmos. Sci., 1963, 20, pp. 130141.
    8. 8)
      • 8. Dachselt, F., Schwarz, W.: ‘Chaos and cryptography’, IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., 2001, 48, pp. 14981501.
    9. 9)
      • 9. Burt, P.J., Adelson, E.H.: ‘The Laplacian pyramid as a compact image coder’, IEEE Trans. Commun., 1983, 31, (4), pp. 532540.
    10. 10)
      • 10. Do, M., Vetterli, M.: ‘Framing pyramids’, IEEE Trans. Signal Process., 2003, 51, (9), pp. 23292342.
    11. 11)
      • 11. Do, M., Vetterli, M.: ‘Contourlets’, In Stoeckler, J., Welland, G.V. (Eds.): ‘Beyond wavelets’ (Academic Press, 2002), pp. 127.
    12. 12)
      • 12. Do, M.: ‘Directional multiresolution image representations’. PhD thesis, Department of Communication Systems, Swiss Federal Institute of Technology Lausanne, 2001.
    13. 13)
      • 13. Kamal, T.M.: ‘Objective tests of speech signal’. MSc thesis, Department of Electrical Engineering, College of Engineering Al-Mustansiriyah University, Iraq, Baghdad, 2001.
    14. 14)
      • 14. ITU-T ITU-TR recommendation P.862: ‘Perceptual evaluation of quality (PESQ): an objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs’, 2001.
    15. 15)
      • 15. Ramdas, V., Mishra, D., Gorthi, S.S.: ‘Speech coding and enhancement using quantized compressive sensing measurements’, Proc. of SPICES- IEEE Conference, 2015, pp. 16.
    16. 16)
      • 16. Gunawan, T.S., Khalifa, O.O., Shafie, A.A., et al: ‘Speech compression using compressive sensing on a multicore system’. Int. Conf. on Mechatronics (ICOM), Kuala Lumpur, Malaysia, 17–19 May 2011.
    17. 17)
      • 17. Kadhim, J.Q., Mahmood, M.K.: ‘Speech scrambling employing Lorenz fractional order chaotic system’, J. Eng. Dev., 2013, 17, (4), pp. 195211.

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