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Distributed Sensor Fields and Uncertainty: Bio-mimetic Methods for Acoustic Source Localization

Distributed Sensor Fields and Uncertainty: Bio-mimetic Methods for Acoustic Source Localization . P. S. Krishnaprasad University of Maryland, College Park Department of Electrical and Computer Engineering

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Distributed Sensor Fields and Uncertainty: Bio-mimetic Methods for Acoustic Source Localization

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  1. Distributed Sensor Fields and Uncertainty: Bio-mimetic Methods for Acoustic Source Localization P. S. Krishnaprasad University of Maryland, College Park Department of Electrical and Computer Engineering & Institute for Systems Research http://www.isr.umd.edu/~krishna ------------ Center for Communicating Networked Control Systems ------------ ARO-MURI01 Review, Boston University October 20-21, 2003

  2. Outline Sensor Field - Motivation Dynamic Sound Source Localization Problems and Models Technical Approach References Demonstration This is joint work with Amir Handzel, Sean Andersson, and Martha Gebremichael. Also thanks to Shihab Shamma for inspiration. Vinay Shah did recent measurements and demos.

  3. Sensor Field A sensor field is heterogeneous (acoustic, seismic, thermal, RF, magnetic, optical…) and often mobile on various platforms (e.g. UGV, UAV…), which are networked and in contact with key gateway nodes - (Eicke/Lavery (1999); Srour (discussions 1998, 2000); NRC (2000) NMAB-495; Emmerman (discussions 2000, 2001); Scanlon/Young (discussions 2003))

  4. Photo: courtesy of Michael Scanlon, ARL

  5. Control over noisy, limited bandwidth, communication channels Intelligent Servosystems Laboratory (ISL)

  6. Dynamic Sound Source Localization - or why we need to move our head? Biologically inspired algorithms

  7. Barn Owl and Robot Can we capture the barn owl’s auditory acuity in a binaural robot?

  8. Localization: spatial aspect of auditory sense Sensory organ arrangement: Vision -- spatial “topographic” Audition -- tonotopic, transduction to sound pressure in frequency bands special computation required, performed in dedicated brainstem circuits and cortex Sound Localization in Nature

  9. Binaural/Inter-aural: Level/Intensity Difference (ILD) Time/Phase Difference (IPD) On-set difference/precedence effect Monaural: spectral-directional filtering by Pinna, mostly for elevation Acoustic Cues for Localization

  10. Place Theory (L. Jeffress)J. Comp. Physiol. & Psychol., (1948) 41:35-39 Jeffress model and schematic of brainstem auditory circuits for detection of interaural time (ITD) differences; from Carr & Amagai (1996)

  11. Stereausis (S. Shamma et. al.)J. Acoust. Soc. Am. (1989) 86:989-1006 AVCN Ipsi- center contra- lateral lateral Yj Ckk +1 Ckk Ckk -1 Characteristic frequency Xi Cij Characteristic frequency Ipsi-lateral cochlea AVCN Sound Contara- lateral cochlea or

  12. Stereausis shifts from the main diagonal according to the source location. -45 deg (left) 0 deg center 45 deg (right) Incoming sound: a pure tone Stereausis scheme (courtesy Shihab Shamma, UMd)

  13. ILD and ITD both needed for azimuth (the concept of HRTF). What about elevation? Lord Rayleigh and Binaural Perception See section 385 of The Theory of Sound 1945 edition 1842-1919

  14. Initial Motivation All the above are static, but real life usually dynamic, and psychophysical experiments show active horizontal head rotations improve localization, break inter-aural symmetry, and thus provide information on elevation (Perret & Noble 1997, Wightman & Kistler 1999). One can explain the above theoretically. Understanding such effects would matter in guiding robots towards acoustic source.

  15. Coordinate Systems • - Azimuthal q- Polar f - Elevationf - Azimuth Microphones at poles on horizontal plane

  16. Pressure field proportional to Does not depend on azimuthal angle (f) Head Related Transfer Function (HRTF) Numerical (e.g. FMP), and empirical methods for non-spherical heads Static Solution

  17. ILD & IPD constitute an intermediate computational space for localization At each frequency a source gives rise to a point in the ILD-IPD plane A (broadband) point source imprints a signature curve on this feature plane (cylinder) according to its location Feature Plane (cylinder) and Signatures

  18. Sound pressure and resulting inter-aural functions depend only on polar angle; azimuth invariant -- SO(2) symmetry Sources on same circle of directions have identical signatures. Hence the localization confusion Symmetry of Static Localization

  19. Symmetry and Rotations • - Azimuthal q- Polar f - Elevationf - Azimuth

  20. Azimuthal invariance, but polar rotations do change the localization functions Key mathematical step: infinitesimal rotations act as derivative operator -- generate vector fields on signatures. Derivatives ‘modulated’ by Cos(f) -- thus elevation extracted from horizontal rotation! Breaking the Symmetry

  21. Experimental Results Broad band source - sum of pure tones 43 Hz – 11 KHz in steps of 43Hz. Passed through anti-aliasing filter and sampled at 22KHz. Knowles FG-3329 microphones used on head of 22.6 cm maximum diameter. To determine ILD and IPD, each 512 point segment (23 ms) of data was passed through an FFT. Measured IPD and ILD were smoothed by a nine-point moving average. This yields empirically determined (discrete) signature curves on ILD-IPD space. Localization computations based on minimizing distance functions. Implementation of this step on mobile robot achieved as a table lookup.

  22. Pumpkin head side-view (left) and top view (right). Minimum diameter 19 cm and maximum diameter 22.6 cm.

  23. Plot on left displays smoothed ILD against theoretical ILD for source at 17.5 degrees in horizontal plane. Plot on right shows smoothed IPD against theoretical IPD for same source.

  24. Plot on left shows distance functions for source at 15 deg and 17.5 deg. Plot on right shows distance functions for source at 72.5 deg and 75 deg.

  25. Performance plots for IPD-ILD algorithm (left) and traditional ITD Algorithm (right)

  26. New experiments in summer 2003 yielded raw data for further investigation of HRTF dependence on elevation. Front-back ambiguity resolution via dynamic IPD-ILD algorithm implemented on robot. (See demo.) Plans to use soldier-helmet from ARL. Photo: Courtesy of Michael Scanlon, ARL

  27. First theoretical analysis and derivation of localization under rotation (no pinnae) Showed analytically that information on elevation can be extracted from active horizontal rotation (in particular front-back) binaurally, with omni-directional sensors. Demonstration in acoustically cluttered environment Accomplishments

  28. Psychophysics: auditory displays, auditory component of virtual environments and hearing aids. Bio-mimetic active robot head Implications and Applications References: A. A. Handzel and P. S. Krishnaprasad, “Bio-mimetic Sound Source Localization”, IEEE Sensors Journal, 2(6), 607-617, 2002. A. A. Handzel, S. B. Andersson, M. Gebremichael, and P. S. Krishnaprasad. “A Bio-mimetic Apparatus for Sound Source Localization”, Proc. 42nd IEEE Conf. on Decision and Control, Dec. 2003 (in press).

  29. Sound following

  30. Front Back Demo Without front-back distinction With front-back distinction

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