Binaural Noise Reduction in the Time Domain

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


Binaural noise reduction is an important problem in applications where there is a need to produce two “clean” outputs from noisy observations picked up by multiple microphones. But the mitigation of the noise should be made in such a way that no audible distortion is added to the two outputs (this is the same as in the single-channel case) and meanwhile the spatial information of the desired sound source should be preserved so that, after noise reduction, the remote listener will still be able to localize the sound source thanks to his/her binaural hearing mechanism. In this chapter, we approach this problem with the widely linear theory in the time domain, where both the temporal and spatial information is exploited.


Noise Reduction Microphone Array Widely Linear TIMIT Database Complex Random Variable 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. Benesty, J. Chen, and Y. Huang, “Binaural noise reduction in the time domain with a stereo setup,” IEEE Trans. Audio, Speech, Language Process., vol. 19, pp. 2260–2272, Nov. 2011.Google Scholar
  2. 2.
    J. Chen and J. Benesty, “A time-domain widely linear MVDR filter for binaural noise reduction,” in Proc. IEEE WASPAA, 2011, pp. 105–108.Google Scholar
  3. 3.
    E. Ollila, “On the circularity of a complex random variable,” IEEE Signal Process. Lett., vol. 15, pp. 841–844, 2008.Google Scholar
  4. 4.
    D. P. Mandic and S. L. Goh, Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models. Wiley, 2009.Google Scholar
  5. 5.
    P. O. Amblard, M. Gaeta, and J. L. Lacoume, “Statistics for complex variables and signals–Part I: variables,” Signal Process., vol. 53, pp. 1–13, 1996.Google Scholar
  6. 6.
    P. O. Amblard, M. Gaeta, and J. L. Lacoume, “Statistics for complex variables and signals–Part II: signals,” Signal Process., vol. 53, pp. 15–25, 1996.Google Scholar
  7. 7.
    J. Benesty, J. Chen, Y. Huang, and I. Cohen, Noise Reduction in Speech Processing. Berlin, Germany: Springer-Verlag, 2009.Google Scholar
  8. 8.
    B. Picinbono and P. Chevalier, “Widely linear estimation with complex data,” IEEE Trans. Signal Process., vol. 43, pp. 2030–2033, Aug. 1995.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

Personalised recommendations