single-rb.php

JRM Vol.29 No.1 pp. 26-36
doi: 10.20965/jrm.2017.p0026
(2017)

Paper:

Noise-Robust MUSIC-Based Sound Source Localization Using Steering Vector Transformation for Small Humanoids

Ryu Takeda and Kazunori Komatani

The Institute of Scientific and Industrial Research, Osaka University
8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Received:
July 20, 2016
Accepted:
November 2, 2016
Published:
February 20, 2017
Keywords:
sound source localization, MUSIC, matrix decomposition, microphone array, robot
Abstract

Noise-Robust MUSIC-Based Sound Source Localization Using Steering Vector Transformation for Small Humanoids

Sound source localization and problem

We focus on the problem of localizing soft/weak voices recorded by small humanoid robots, such as NAO. Sound source localization (SSL) for such robots requires fast processing and noise robustness owing to the restricted resources and the internal noise close to the microphones. Multiple signal classification using generalized eigenvalue decomposition (GEVD-MUSIC) is a promising method for SSL. It achieves noise robustness by whitening robot internal noise using prior noise information. However, whitening increases the computational cost and creates a direction-dependent bias in the localization score, which degrades the localization accuracy. We have thus developed a new implementation of GEVD-MUSIC based on steering vector transformation (TSV-MUSIC). The application of a transformation equivalent to whitening to steering vectors in advance reduces the real-time computational cost of TSV-MUSIC. Moreover, normalization of the transformed vectors cancels the direction-dependent bias and improves the localization accuracy. Experiments using simulated data showed that TSV-MUSIC had the highest accuracy of the methods tested. An experiment using real recoded data showed that TSV-MUSIC outperformed GEVD-MUSIC and other MUSIC methods in terms of localization by about 4 points under low signal-to-noise-ratio conditions.

References
  1. [1] K. Nakadai, T. Lourens, H. G. Okuno, and H. Kitano, “Active audition for humanoid,” Proc. of the Seventeenth National Conference on Artificial Intelligence, pp. 832-839, 2000.
  2. [2] D. Gouaillier, V. Hugel, P. Blazevic, C. Kilner, J. O. Monceaux, P. Lafourcade, B. Marnier, J. Serre, and B. Maisonnier, “Mechatronic design of Nao humanoid,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 769-774, 2009.
  3. [3] T. Miyazaki, M. Mizumachi, and K. Niyada, “Acoustic analysis of breathy and rough voice characterizing elderly speech,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.14, No.2, pp. 135-141, 2010.
  4. [4] K. Nakadai, H. G. Okuno, H. Nakajima, Y. Hasegawa, and H. Tsujino, “An open source software system for robot audition HARK and its evaluation,” Proc. of IEEE-RAS Int. Conf. on Humanoid Robots, pp. 561-566, 2008.
  5. [5] K. Nakamura, K. Nakadai, and G. Ince, “Real-time super-resolution sound source localization for robots,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 694-699, 2012.
  6. [6] R. O. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Trans. on Antennas and Propagation, Vol.AP-32, No.3, pp. 276-280, 1986.
  7. [7] S. Argentieri, P. Danès, and P. Souères, “A survey on sound source localization in robotics: From binaural to array processing methods,” Computer Speech & Language, Vol.34, No.1, pp. 87-112, 2015.
  8. [8] K. Nakamura, N. Kazuhiro, F. Asano, Y. Hasegawa, and H. Tsujino, “Intelligent sound source localization for dynamic environment,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 664-669, 2009.
  9. [9] T. Ohata, K. Nakamura, T. Mizumoto, T. Tezuka, and K. Nakadai, “Improvement in outdoor sound source detection using a quadrotor-embedded microphone array,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1902-1907, 2014.
  10. [10] R. Takeda and K. Komatani, “Performance comparison of MUSIC-based sound localization methods on small humanoid under low SNR conditions,” Proc. of IEEE-RAS Int. Conf. on Humanoid Robots, pp. 859-865, 2015.
  11. [11] H. Krim and M. Viberg, “Two decades of array signal processing research: The parametric approach,” Signal Processing Magazine, Vol.13, No.4, pp. 67-94, 1996.
  12. [12] J. Capon. “High-resolution frequency-wavenumber spectrum analysis,” Proc. of IEEE, Vol.57, No.8, pp. 1408-1418, 1969.
  13. [13] K. Nakadai, K. Hidai, H. G. Okuno, H. Mizoguchi, and H. Kitano, “Real-time auditory and visual multiple-speaker tracking for human-robot interaction,” J. of Robotics and Mechatronics, Vol.14, No.5, pp. 479-489, 2002.
  14. [14] C. Knapp and G. Carter, “The generalized correlation method for estimation of time delay,” IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol.24, No.4, pp. 320-327, 1976.
  15. [15] A. Badali, J.-M. Valin, F. Michaud, and P. Aarabi, “Evaluating real-time audio localization algorithms for artificial audition in robotics,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2033-2038, 2009.
  16. [16] R. Roy and T. Kailath, “ESPRIT – estimation of signal parameters via rotational invariance techniques,” IEEE Trans. Acoust., Speech, Signal Processing, Vol.37, No.7, pp. 984-995, 1989.
  17. [17] M. J. Taghizadeh, S. Haghighatshoar, A. Asaei, P. N. Garner, and H. Bourlard, “Robust microphone placement for source localization from noisy distance measurements,” Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 2579-2583, 2015.
  18. [18] R. Takeda, K. Nakadai, K. Komatani, T. Ogata, and H. G. Okuno, “Exploiting known sound sources to improve ICA-based robot audition in speech separation and recognition,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1757-1762, 2007.
  19. [19] T. Nakatani, T. Yoshioka, K. Kinoshita, M. Miyoshi, and B.-H. Juang, “Blind speech dereverberation with multi-channel linear prediction based on short time Fourier transform representation,” Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 85-88, 2008.
  20. [20] S. Argentieri and P. Danes, “Broadband variations of the music high-resolution method for sound source localization in robotics,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2009-2014, 2007.
  21. [21] F. Asano, H. Asoh, and K. Nakadai, “Sound source localization using joint Bayesian estimation with a hierarchical noise model,” IEEE Trans. on Audio, Speech and Language Processing, Vol.21, No.9, pp. 1953-1965, 2013.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 20, 2017