Conceptual Framework

  • Jacob BenestyEmail author
  • Jingdong Chen
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)


In this chapter, a conceptual framework for noise reduction is proposed. This new formulation gives a better insight into this fundamental problem. Within this framework, we define all important performance measures and criteria that will be of great help in the derivation of the most well-known estimators. Some key discussions concern also the definitions of speech intelligibility and speech quality that will be used in the rest of this work.


Noise Reduction Speech Quality Speech Enhancement Error Concealment Speech Intelligibility 
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  1. 1.
    J. Benesty, J. Chen, Y. Huang, and I. Cohen, Noise Reduction in Speech Processing. Berlin, Germany: Springer-Verlag, 2009.Google Scholar
  2. 2.
    P. Vary and R. Martin, Digital Speech Transmission: Enhancement, Coding and Error Concealment. Chichester, England: John Wiley & Sons Ltd, 2006.Google Scholar
  3. 3.
    P. Loizou, Speech Enhancement: Theory and Practice. Boca Raton, FL: CRC Press, 2007.Google Scholar
  4. 4.
    N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series. New York: John Wiley & Sons, 1949.Google Scholar
  5. 5.
    J. Benesty, J. Chen, Y. Huang, and S. Doclo, “Study of the Wiener filter for noise reduction,” in Speech Enhancement, J. Benesty, S. Makino, and J. Chen, Eds., Berlin, Germany: Springer-Verlag, 2005, Chapter 2, pp. 9–41.Google Scholar
  6. 6.
    J. Chen, J. Benesty, Y. Huang, and S. Doclo, “New insights into the noise reduction Wiener filter,” IEEE Trans. Audio, Speech, Language Process., vol. 14, pp. 1218–1234, July 2006.Google Scholar
  7. 7.
    J. Capon, “High resolution frequency-wavenumber spectrum analysis,” Proc. IEEE, vol. 57, pp. 1408–1418, Aug. 1969.Google Scholar
  8. 8.
    R. T. Lacoss, “Data adaptive spectral analysis methods,” Geophysics, vol. 36, pp. 661–675, Aug. 1971.Google Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  1. 1.INRS-EMT, University of QuebecMontrealCanada
  2. 2.Northwestern Polytechnical UniversityXi’an, ShaanxiChina

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