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Applied Intelligence

, Volume 42, Issue 1, pp 63–74 | Cite as

Improved sound source localization in horizontal plane for binaural robot audition

  • Ui-Hyun Kim
  • Kazuhiro Nakadai
  • Hiroshi G. Okuno
Article

Abstract

An improved sound source localization (SSL) method has been developed that is based on the generalized cross-correlation (GCC) method weighted by the phase transform (PHAT) for use with binaural robots equipped with two microphones inside artificial pinnae. The conventional SSL method based on the GCC-PHAT method has two main problems when used on a binaural robot platform: 1) diffraction of sound waves with multipath interference caused by the contours of the robot head, which affects localization accuracy, and 2) front-back ambiguity, which limits the localization range to half the horizontal space. The diffraction problem was overcome by incorporating a new time delay factor into the GCC-PHAT method under the assumption of a spherical robot head. The ambiguity problem was overcome by utilizing the amplification effect of the pinnae for localization over the entire azimuth. Experiments conducted using two dummy heads equipped with small or large pinnae showed that localization errors were reduced by 8.91° (3.21° vs. 12.12°) on average with the new time delay factor compared with the conventional GCC-PHAT method and that the success rate for front-back disambiguation using the pinnae amplification effect was 29.76 % (93.46 % vs. 72.02 %) better on average over the entire azimuth than with a conventional head related transfer function (HRTF)-based method.

Keywords

Intelligent robot audition Human-robot interaction Sound source localization Front-back disambiguation 

Notes

Acknowledgments

This research was partially supported by a Grant-in-Aid for Scientific Research (KAKENHI No. 24220006) from the Japan Society for the Promotion of Science (JSPS).

References

  1. 1.
    Starch D (1908) Perimetry of the localization of sound. State University of IowaGoogle Scholar
  2. 2.
    Bregman AS (1990) Auditory scene analysis. MIT Press, CambridgeGoogle Scholar
  3. 3.
    Dautenhahn K (2007) Socially intelligent robots: dimensions of human-robot interaction. Philos Trans R Soc B: Biol Sci 362(1480):679–704CrossRefGoogle Scholar
  4. 4.
    Valin JM, Michaud F, Rouat J, Letouneau D (2003) Robust sound source localization using a microphone array on a mobile robot. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS). Las Vegas, pp 1128–1233Google Scholar
  5. 5.
    Tamai Y, Sasaki Y, Kagami S, Mizoguchi H (2005) Three ring microphone array for 3D sound localization and separation for mobile robot audition. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS). Alberta, pp 4172–4177Google Scholar
  6. 6.
    Kim UH, Kim J, Kim D, Kim H, You B J(200) Speaker localization using the TDOA-based feature matrix for a humanoid robot. In: Proceedings of the IEEE international symposium on robot and human interactive communication (RO-MAN). Munich, pp 610–615Google Scholar
  7. 7.
    Hu JS, Chan CY, Wang CK, Wang CC (2009) Simultaneous localization of mobile robot and multiple sound sources using microphone array. In: Proceedings of the IEEE international conference on robots and automation (ICRA). Kobe, pp 2934Google Scholar
  8. 8.
    Li X, Liu H, Yang X (2011) Sound source localization for mobile robot based on time difference feature and space grid matching. In: Proceedings of the IEEE/RSJ international robotics and systems (IROS). San Francisco, pp 2879–2886Google Scholar
  9. 9.
    Sasaki Y, Kabasawa M, Thompson S, Kagami S, Oro K (2012) Spherical microphone array for spatial sound localization for a mobile robot. In: Proceedings of the international conference on intelligent robots and systems (IROS). Algarve, pp 713–718Google Scholar
  10. 10.
    Blauert J (1997) Spatial hearing: The psychophysics of human sound localization (Revised Edition). Cambridge. MIT PressGoogle Scholar
  11. 11.
    Wallach H, Newman EB, Rosenzweig MR (1949) The precedence effect in sound localization. Am J Psychol 62(3):315–336CrossRefGoogle Scholar
  12. 12.
    Blauert J, Braasch J (2011) Binaural signal processing. In: Proceedings of the IEE international conference on digital signal processing (DSP). Greece, pp 1–11Google Scholar
  13. 13.
    Rodemann T (2010) A study on distance estimation in binaural sound localization. In: Proccedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS). Offenback, pp 425–430Google Scholar
  14. 14.
    Youssef K, Argentieri S, Zarader JL (2012) Toward a systematic study of binaural cues. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS). Villamoura, pp 1004–1009Google Scholar
  15. 15.
    Cheng CI, Wakefield GH (2001) Introduction to head-related transfer functions (HRTFs): representations of HRTFs in time, frequency, and space. Audio Eng Soc 49:231–249Google Scholar
  16. 16.
    Moore BCJ (2003) An introduction to the psychology of hearing, 5th edn. Academic PressGoogle Scholar
  17. 17.
    Wang DL, Brown GJ (2006) Computational auditory sceneanalysis: principles, algorithms, and applications. Wiley InterScienceGoogle Scholar
  18. 18.
    Carter GC, Nuttall AA, Cable PG (1973) The smoothed coherence transform. In: Proccedings of the IEEE 61(10):1497–1498CrossRefGoogle Scholar
  19. 19.
    Knapp C H, Carter G C (1976) The generalized correlation method for estimation of time delay. IEEE Trans Acoust Speech Sig Process 24(4):320–327CrossRefGoogle Scholar
  20. 20.
    Hassab JC, Boucher RE (1979) Optimum estimation of time delay by a generalized correlator. IEEE T-ASSP 27(4):373–380CrossRefMATHGoogle Scholar
  21. 21.
    Wallach H (1940) The role of head movements and vestibular and visual cues in sound localization. J Exp Psychol 27(4):339–368CrossRefMathSciNetGoogle Scholar
  22. 22.
    Hill PA, Nelson PA, Kirkeby O, Hamada H (2000) Resolution of front-back confusion in virtual acoustic imaging systems. Acoust Soc Am 108(6):2901–2910CrossRefGoogle Scholar
  23. 23.
    Nakashima H, Mukai T (2005) 3D sound source localization system based on learning of Binaural hearing. In: Proceedings of the IEEE international conference on systems, man and cybernetics (SMC). Nagoya, vol. 4, pp. 3534–3539Google Scholar
  24. 24.
    Ovcharenko A, Cho SJ, Chonga UP (2007) Front-back confusion resolution in three-dimensional sound localization using databases built with a dummy head. J Acoust Soc Am 122(1):489–495CrossRefGoogle Scholar
  25. 25.
    Rodemann T, Ince G, Joublin F, Goerick C (2008) Using binaural and spectral cues for azimuth and elevation localization. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS). Nice, pp 2185–2190Google Scholar
  26. 26.
    Kim UH, Nakadai K, Okuno HG (2013) Improved sound source localization and front-back disambiguation for humanoid robots with two ears, In: Proccedings of internationales conference on industrial engineering and other applications of applied intelligent systems (IEA/AIE). Amsterdam, pp 282–291Google Scholar
  27. 27.
    Algazi VR, Duda RO, Thompson DM, Avendano C (2001) The CIPIC HRTF database. In: Procedings of the IEEE international worker on applications of signal processing to audio and electroacoustics. New Paltz, New York, pp 99–102Google Scholar
  28. 28.
    Jian M, Kot AC, Er MH (1998) DOA estimation of speech source with microphone arrays, vol 5l. MontereyGoogle Scholar
  29. 29.
    Kim UH, Okuno HG (2013) Improved binaural sound localization and tracking for unknown time-varying number of speakers. Adv Robot 27(15):1161–1173CrossRefGoogle Scholar
  30. 30.
    Kim UH, Okuno HG (2013) Robust localization and tracking of multiple speakers in real environments for binaural robot audition. In: Procedings of international worker on image and audio analysis for multimedia interactive services (WIA 2MIS). France, pp 1–4Google Scholar
  31. 31.
    Hassab JC, Boucher RE (1981) Performance of the generalized cross correlator in the presence of a strong spectral peak in the signal. IEEE T-ASSP 29(3):549–555CrossRefGoogle Scholar
  32. 32.
    Azaria M, Hertz D (1984) Time delay estimation by generalized cross correlation methods. IEEE Trans Acoust Speech Sig Process 32(2):280–285CrossRefGoogle Scholar
  33. 33.
    Lim JS, Oppenheim AV (1979) Enhancement and bandwith compression of noisy speech. In: Proc. IEEE 67(12):1586–1604CrossRefGoogle Scholar
  34. 34.
    Middlebrooks JC (1991) Sound localization by human listeners. Annu Rev Psychol 42:135–159CrossRefGoogle Scholar
  35. 35.
    Suzuki Y, Asano F, Kim HY, Sone T (1995) An optimum computer-generated pulse signal suitable for the measurement of very long impulse responses. ACM 97(2):1119–1123Google Scholar
  36. 36.
    Sohn J., Sung W (1998) A voice activity detector employing soft decision based noise spectrum adaptation. In: Proceedings of IEEE International Conference Acoustic Speech Signal Process (ICASSP), pp 365368Google Scholar
  37. 37.
    Sohn J, Kim NS, Sung W (1999) A statistical model-based voice activity detection. Sig Process Lett 6(1):1–3CrossRefGoogle Scholar
  38. 38.
    Kim T, Attias T, Lee SY (2007) Blind source separation exploiting higher-order frequency dependencies.IEEE Trans Audio Speech Lang Process 15(1):70–79CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ui-Hyun Kim
    • 1
  • Kazuhiro Nakadai
    • 2
  • Hiroshi G. Okuno
    • 1
  1. 1.Department of Intelligence Science and TechnologyGraduate School of Informatics Kyoto UniversityKyoto-shiJapan
  2. 2.Honda Research Institute Japan Co., Ltd.Wako-shiJapan

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