Principles of Entropy Coding with Perceptual Quality Evaluation

  • Tom BäckströmEmail author
Part of the Signals and Communication Technology book series (SCT)


The objective of speech coding is to transmit speech at the highest possible quality with the lowest possible amount of resources. To achieve the best compromise, we can use available information about 1. the source, which is the speech production system, 2. the quality measure or evaluation criteria, which depends on the performance of the human hearing system and 3. the statistical frequency and distribution of the involved parameters. By developing models for all such information, we can optimise the system to perform efficiently. In practice, the three methods are overlapping in the sense that it is often difficult to make a clear-cut separation between them. While source modelling was already discussed in Chap.  2, this chapter reviews entropy coding methods and the associated perceptual modelling methods.


Discrete Cosine Transform Entropy Code Differential Entropy Quantisation Accuracy Uniform Quantisation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming: Theory and Algorithms. Wiley, New Jersey (2013)zbMATHGoogle Scholar
  2. 2.
    Bosi, M., Goldberg, R.E.: Introduction to Digital Audio Coding and Standards. Kluwer Academic Publishers, Dordrecht (2003)CrossRefGoogle Scholar
  3. 3.
    Bäckström, T.: Vandermonde factorization of Toeplitz matrices and applications in filtering and warping. IEEE Trans. Signal Process. 61(24), 6257–6263 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bäckström, T., Helmrich, C.R.: Decorrelated innovative codebooks for ACELP using factorization of autocorrelation matrix. In: Proceedings of the Interspeech, pp. 2794–2798 (2014)Google Scholar
  5. 5.
    Edler, B.: Coding of audio signals with overlapping block transform and adaptive window functions. Frequenz 43(9), 252–256 (1989)CrossRefGoogle Scholar
  6. 6.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Springer, New York (1992)zbMATHCrossRefGoogle Scholar
  7. 7.
    Gibson, J.D., Sayood, K.: Lattice quantization. Adv. Electron. Electron Phys. 72, 259–330 (1988)CrossRefGoogle Scholar
  8. 8.
    Golub, G.H., van Loan, C.F.: Matrix Computations, 3rd edn. John Hopkins University Press, Maryland (1996)zbMATHGoogle Scholar
  9. 9.
    Gray, R.M., Neuhoff, D.L.: Quantization. IEEE Trans. Inf. Theory 44(6), 2325–2383 (1998)zbMATHCrossRefGoogle Scholar
  10. 10.
    Jayant, N.S., Noll, P.: Digital Coding of Waveforms: Principles and Applications to Speech and Video. Englewood Cliffs, New Jersey (1984)Google Scholar
  11. 11.
    Mitra, S.K.: Digital Signal Processing: A Computer-Based Approach. McGraw-Hill, Boston (1998)Google Scholar
  12. 12.
    Pisoni, D., Remez, R.: The Handbook of Speech Perception. Wiley, New Jersey (2008)Google Scholar
  13. 13.
    Rabiner, L.R., Schafer, R.W.: Digital Processing of Speech Signals. Prentice-Hall, Englewood Cliffs (1978)Google Scholar
  14. 14.
    Sanchez, V.E., Adoul, J.-P.: Low-delay wideband speech coding using a new frequency domain approach. In: Proceedings of the ICASSP, vol. 2, pp. 415–418. IEEE (1993)Google Scholar
  15. 15.
    Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.International Audio Laboratories Erlangen (AudioLabs)Friedrich-Alexander University Erlangen-Nürnberg (FAU)ErlangenGermany

Personalised recommendations