Audio Coding pp 199-229 | Cite as


  • Yuli You


An audio signal often consists of quasistationary episodes, each including a number of tonal frequency components, which are frequently interrupted by dramatic transients. To achieve optimal energy compaction and thus coding gain, a filter bank with fine frequency resolution is necessary to resolve the tonal components or fine frequency structures in quasistationary episodes. But this filter bank is an ill fit for transients which often last for no more than a few samples, hence require fine time resolution for optimal energy compaction. Therefore, filter banks with both good time and frequency resolution are needed to effectively code audio signals.


Entropy Compaction Autocorrelation Sine Acoustics 
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.


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  1. 10.
    Davidson, G., Bosi, M.: AC-2: high quality audio coding for broadcasting and storage. 46th Annual Broadcasting Engineering Conference pp. 98–105 (1992)Google Scholar
  2. 11.
    Dolby Laboratories: Digital Audio Compression Standard A/52B. Advanced Television Systems Committee (ATSC) (2005)Google Scholar
  3. 26.
    Herre, J., Johnston, J.D.: Enhancing the performance of perceptual audio coders by using temporal noise shaping (TNS). 101st AES Convention (1996 Reprint #4384)Google Scholar
  4. 34.
    Johnston, J.D.: Transform coding of audio signals using perceptual noise criteria. IEEE Journal on Selected Areas in Communications 6(2), 314–323 (1988)CrossRefGoogle Scholar
  5. 36.
    Johnston, J.D., Sinha, D., Dorward, S., Quackenbush, S.: AT&T perceptual audio coding (PAC). Collected Papers on Digital Audio Bit-Rate Reduction pp. 73–81 (1996)Google Scholar
  6. 55.
    MPEG: Coding of moving pictures and associated audio for digital storage media at up to about 1.5 Mbit/s – Part 3: Audio, vol. 11172-3. ISO/IEC (1993)Google Scholar
  7. 56.
    MPEG: Information technology: generic coding of moving pictures and associated audio information – Part 3: Audio, vol. 13818-3. ISO/IEC (1998)Google Scholar
  8. 59.
    MPEG: Coding of Audio-Visual Objects: Audio, vol. 14496-3. ISO/IEC (2005)Google Scholar
  9. 60.
    MPEG: Information technology: Generic coding of moving pictures and associated audio information Part 7: Advanced Audio Coding (AAC), vol. 13818-7. ISO/IEC (2006)Google Scholar
  10. 69.
    Painter, T., Spanias, A.: Perceptual coding of digital audio. Proceedings of the IEEE 88(4), 451–513 (2000)CrossRefGoogle Scholar
  11. 75.
    Pinsky, M.A.: Introduction to Fourier Analysis and Wavelets. American Mathematical Society (2009)Google Scholar
  12. 87.
    Sinha, D., Johnston, J.D.: Audio compression at low bit rates using a signal adaptive switched filter bank. IEEE International Conference on Acoustics, Speech, and Signal Processing 2, 1053–1056 (1996)Google Scholar
  13. 88.
    Smyth, M.: White Paper: An Overview of the Coherent Acoustics Coding System. DTS, Agoura Hills (1999)Google Scholar
  14. 90.
    Stein, E., Shakarchi, R.: Fourier Analysis: An introduction. Princeton University Press, Princeton (2003)MATHGoogle Scholar
  15. 92.
    Tsutsui, K.: ATRAC (adaptive transform acoustic coding) and ATRAC 2. In: V. Madisetti and D. Williams (eds.) The Digital Signal Processing Handbook, pp. 43.16–43.20. CRC, Boca Raton (1998)Google Scholar
  16. 95.
    Wikipedia: Windows Media Audio. (2007)
  17. 96., F.: Vorbis I specification. Foundation (2004)Google Scholar
  18. 98.
    You, Y.L. et al.: Multichannel Digital Audio Coding Technology, vol. SJ/T11368-2006. Ministry of Information Industry, People’s Republic of China (2007)Google Scholar

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© Springer US 2010

Authors and Affiliations

  • Yuli You
    • 1
  1. 1.University of Minnesota in Twin CitiesMinneapolisUSA

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