Skip to content


  • Research Article
  • Open Access

Using Intermicrophone Correlation to Detect Speech in Spatially Separated Noise

  • 1 and
  • 2
EURASIP Journal on Advances in Signal Processing20062006:093920

  • Received: 29 April 2004
  • Accepted: 25 April 2005
  • Published:


This paper describes a system for determining intervals of "high" and "low" signal-to-noise ratios when the desired signal and interfering noise arise from distinct spatial regions. The correlation coefficient between two microphone signals serves as the decision variable in a hypothesis test. The system has three parameters: center frequency and bandwidth of the bandpass filter that prefilters the microphone signals, and threshold for the decision variable. Conditional probability density functions of the intermicrophone correlation coefficient are derived for a simple signal scenario. This theoretical analysis provides insight into optimal selection of system parameters. Results of simulations using white Gaussian noise sources are in close agreement with the theoretical results. Results of more realistic simulations using speech sources follow the same general trends and illustrate the performance achievable in practical situations. The system is suitable for use with two microphones in mild-to-moderate reverberation as a component of noise-reduction algorithms that require detecting intervals when a desired signal is weak or absent.


  • Probability Density Function
  • Decision Variable
  • Bandpass Filter
  • White Gaussian Noise
  • Noise Source

Authors’ Affiliations

Broadband Video Compression Group, Broadcom Corporation, Andover, MA 01810, USA
Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room E25-518, Cambridge, MA 02139-4307, USA


  1. Plomp R: Auditory handicap of hearing impairment and the limited benefit of hearing aids. Journal of the Acoustical Society of America 1978, 63(2):533–549. 10.1121/1.381753View ArticleGoogle Scholar
  2. Smedley TC, Schow RL: Frustrations with hearing aid use: candid reports from the elderly. The Hearing Journal 1990, 43(6):21–27.Google Scholar
  3. Kochkin S: MarkeTrak V: consumer satisfaction revisited. The Hearing Journal 2000, 53(1):38–55.View ArticleGoogle Scholar
  4. Kochkin S: MarkeTrak V: 'why my hearing aids are in the drawer': the consumers' perspective. The Hearing Journal 2000, 53(2):34–42.View ArticleGoogle Scholar
  5. Van Compernolle D: Hearing aids using binaural processing principles. Acta Oto-Laryngologica: Supplement 1990, 469: 76–84.Google Scholar
  6. Kompis M, Dillier N: Noise reduction for hearing aids: Combining directional microphones with an adaptive beamformer. Journal of the Acoustical Society of America 1994, 96(3):1910–1913. 10.1121/1.410204View ArticleGoogle Scholar
  7. Greenberg JE, Zurek PM: Evaluation of an adaptive beamforming method for hearing aids. Journal of the Acoustical Society of America 1992, 91(3):1662–1676. 10.1121/1.402446View ArticleGoogle Scholar
  8. Van Compernolle D, Ma W, Xie F, Van Diest M: Speech recognition in noisy environments with the aid of microphone arrays. Speech Communication 1990, 9(5–6):433–442. 10.1016/0167-6393(90)90019-6View ArticleGoogle Scholar
  9. Kobatake H, Tawa K, Ishida A: Speech/nonspeech discrimination for speech recognition system under real life noise environments. Proc IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '89), May 1989, Glasgow, Scotland, UK 1: 365–368.Google Scholar
  10. Freeman DK, Cosier G, Southcott CB, Boyd I: The voice activity detector for the Pan-European digital cellular mobile telephone service. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '89), May 1989, Glasgow, Scotland, UK 1: 369–372.Google Scholar
  11. Marzinzik M, Kollmeier B: Speech pause detection for noise spectrum estimation by tracking power envelope dynamics. IEEE Transactions on Speech and Audio Processing 2002, 10(2):109–118. 10.1109/89.985548View ArticleGoogle Scholar
  12. Breining C, Dreiscitel P, Hansler E, et al.: Acoustic echo control. An application of very-high-order adaptive filters. IEEE Signal Processing Magazine 1999, 16(4):42–69. 10.1109/79.774933View ArticleGoogle Scholar
  13. Stegmann J, Schroder G: Robust voice-activity detection based on the wavelet transform. Proceedings of IEEE Workshop on Speech Coding For Telecommunications Proceeding, September 1997, Pocono Manor, Pa, USA 99–100.Google Scholar
  14. Tucker R: Voice activity detection using a periodicity measure. IEE Proceedings. I: Communications, Speech, and Vision 1992, 139(4):377–380. 10.1049/ip-i-2.1992.0052Google Scholar
  15. Pencak J, Nelson D: The NP speech activity detection algorithm. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '95), May 1995, Detroit, Mich, USA 1: 381–384.Google Scholar
  16. Hoyt JD, Wechsler H: Detection of human speech in structured noise. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '94), April 1994, Adelaide, Australia 2: 237–240.Google Scholar
  17. Sims JT: A speech-to-noise ratio measurement algorithm. Journal of the Acoustical Society of America 1985, 78(5):1671–1674. 10.1121/1.392806View ArticleGoogle Scholar
  18. Akagi M, Kago T: Noise reduction using a small-scale microphone array in multi noise source environment. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 1: 909–912.Google Scholar
  19. Hoffman MW, Li Z, Khataniar D: GSC-based spatial voice activity detection for enhanced speech coding in the presence of competing speech. IEEE Transactions Speech Audio Processing 2001, 9(2):175–178. 10.1109/89.902284View ArticleGoogle Scholar
  20. Le Bouquin-Jeannès R, Faucon G: Study of a voice activity detector and its influence on a noise reduction system. Speech Communication 1995, 16(3):245–254. 10.1016/0167-6393(94)00056-GView ArticleGoogle Scholar
  21. Kompis M, Dillier N, Francois J, Tinembart J, Hausler R: New target-signal-detection schemes for multi-microphone noise-reduction systems for hearing aids. Proceedings of 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '97), October–November 1997, Chicago, Ill, USA 5: 1990–1993.Google Scholar
  22. van Hoesel RJM, Clark GM: Evaluation of a portable two-microphone adaptive beamforming speech processor with cochlear implant patients. Journal of the Acoustical Society of America 1995, 97(4):2498–2503. 10.1121/1.411970View ArticleGoogle Scholar
  23. Janecek P: A model for the sound energy distribution in work spaces based on the combination of direct and diffuse sound fields. Acustica 1991, 74: 149–156.Google Scholar
  24. Bulmer MG: Principles of Statistics. Dover, New York, NY, USA; 1979.MATHGoogle Scholar
  25. Hartmann WM: Signals, Sound, and Sensation. Springer, New York, NY, USA; 1998.View ArticleGoogle Scholar
  26. Hsu HP: Probability, Random Variables, and Random Processes. McGraw-Hill, New York, NY, USA; 1997.Google Scholar
  27. Nélisse H, Nicolas J: Characterization of a diffuse field in a reverberant room. Journal of the Acoustical Society of America 1997, 101(6):3517–3524. 10.1121/1.418313View ArticleGoogle Scholar
  28. Van Trees HL: Detection, Estimation, and Modulation Theory, Part I. John Wiley & Sons, New York, NY, USA; 1968.MATHGoogle Scholar
  29. Allen JB, Berkley DA: Image method for efficiently simulating small-room acoustics. Journal of the Acoustical Society of America 1979, 65(4):943–950. 10.1121/1.382599View ArticleGoogle Scholar
  30. Peterson PM: Simulating the response of multiple microphones to a single acoustic source in a reverberant room. Journal of the Acoustical Society of America 1986, 80(5):1527–1529. 10.1121/1.394357View ArticleGoogle Scholar
  31. IEEE : IEEE recommended practice for speech quality measurements. In Tech. Rep. IEEE 297. Institute of Electrical and Electronics Engineers, Washington, DC, USA; 1969.Google Scholar
  32. Kalikow DN, Stevens KN, Elliot LL: Development of a test of speech intelligibility in noise using sentence materials with controlled word predictability. Journal of the Acoustical Society of America 1977, 61(5):1337–1351. 10.1121/1.381436View ArticleGoogle Scholar


© A. Koul and J. E. Greenberg. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.