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Acoustic Beamforming with Maximum SNR Criterion and Efficient Generalized Eigenvector Tracking

  • Toshihisa Tanaka
  • Mitsuaki Shiono
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8879)

Abstract

A recently proposed adaptive acoustic beamformer based on the maximization of the output SNR (Max-SNR beamformer) has an advantage of requiring no information of transfer functions. A key technology to implement Max-SNR beamformers is to estimate generalized eigenvector (GEV) of covariance matrices of target signal and noise, which are basically unknown. We develop a novel GEV tracking algorithm with decaying time windows that enable Max-SNR beamformer to adapt rapidly moving sources. Simulation results support the analysis.

Keywords

Direction Cosine Microphone Array Generalize Eigenvector Microphone Signal Gradient Ascent 
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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References

  1. 1.
    Allen, J.B., Berkley, D.A.: Image method for efficiently simulating small-room acoustics. The Journal of the Acoustical Society of America 65, 943–950 (1979)CrossRefGoogle Scholar
  2. 2.
    Fudge, G.L., Linebarger, D.A.: A calibrated generalized sidelobe canceller for wideband beamforming. IEEE Trans. Signal Process. 42(10), 2871–2875 (1994)CrossRefGoogle Scholar
  3. 3.
    Greenberg, J.E., Zurek, P.M.: Evaluation of an adaptive beamforming method for hearing aids. The Journal of the Acoustical Society of America 91, 1662–1676 (1992)CrossRefGoogle Scholar
  4. 4.
    Griffiths, L., Jim, C.: An alternative approach to linearly constrained adaptive beamforming. IEEE Trans. Antennas Propag. 30(1), 27–34 (1982)CrossRefGoogle Scholar
  5. 5.
    Habets, E., Benesty, J., Cohen, I., Gannot, S., Dmochowski, J.: New insights into the MVDR beamformer in room acoustics. IEEE Trans. Audio, Speech, and Language Process. 18(1), 158–170 (2010)CrossRefGoogle Scholar
  6. 6.
    Kolossa, D., Araki, S., Delcroix, M., Nakatani, T., Orglmeister, R., Makino, S.: Missing feature speech recognition in a meeting situation with maximum SNR beamforming. In: Proc. IEEE Int. Symp. Circuits Syst. (ISCAS 2008), pp. 3218–3221 (2008)Google Scholar
  7. 7.
    Kompis, M., Dillier, N.: Noise reduction for hearing aids: Combining directional microphones with an adaptive beamformer. The Journal of the Acoustical Society of America 96, 1910 (1994)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Seltzer, M.L., Raj, B., Stern, R.M.: Likelihood-maximizing beamforming for robust hands-free speech recognition. IEEE Trans. Speech Audio Process. 12(5), 489–498 (2004)CrossRefGoogle Scholar
  10. 10.
    Sohn, J., Kim, N.S., Sung, W.: A statistical model-based voice activity detection. IEEE Signal Process. Lett. 6(1), 1–3 (1999)CrossRefGoogle Scholar
  11. 11.
    Sohn, J., Sung, W.: A voice activity detector employing soft decision based noise spectrum adaptation. In: Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP 1998), vol. 1, pp. 365–368 (1998)Google Scholar
  12. 12.
    Souden, M., Benesty, J., Affes, S.: A study of the lcmv and MVDR noise reduction filters. IEEE Trans. Signal Process. 58(9), 4925–4935 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Tanaka, T.: Fast generalized eigenvector tracking based on the power method. IEEE Signal Process. Lett. 16(11), 969–972 (2009)CrossRefGoogle Scholar
  14. 14.
    Warsitz, E., Haeb-Umbach, R.: Blind acoustic beamforming based on generalized eigenvalue decomposition. IEEE Trans. Audio, Speech, and Language Process. 15(5), 1529–1539 (2007)CrossRefGoogle Scholar
  15. 15.
    Yang, B.: Projection approximation subspace tracking. IEEE Trans. Signal Process. 43(1), 95–107 (1995)CrossRefzbMATHGoogle Scholar
  16. 16.
    Yang, J., Zhao, Y., Xi, H.: Weighted rule based adaptive algorithm for simultaneously extracting generalized eigenvectors. IEEE Transactions on Neural Networks 22(5), 800–806 (2011)CrossRefGoogle Scholar
  17. 17.
    Yang, J., Zhao, Y., Xi, H.: Weighted rule based adaptive algorithm for simultaneously extraction generalized eigenvectors. IEEE Trans. Neural Netw. 22(5), 800–806 (2011)CrossRefGoogle Scholar
  18. 18.
    Zhang, C., Florêncio, D., Ba, D.E., Zhang, Z.: Maximum likelihood sound source localization and beamforming for directional microphone arrays in distributed meetings. IEEE Trans. Multimedia 10(3), 538–548 (2008)CrossRefGoogle Scholar
  19. 19.
    Zheng, Y., Goubran, R., El-Tanany, M., Shi, H.: A microphone array system for multimedia applications with near-field signal targets. IEEE Sensors Journal 5(6), 1395–1406 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Toshihisa Tanaka
    • 1
  • Mitsuaki Shiono
    • 1
  1. 1.Tokyo University of Agriculture and TechnologyKoganei-shiJapan

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