Boston University--Harvard University--University of Illinois--University of Maryland Distributed...

Boston University--Harvard University--University of Illinois--University of Maryland

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


Sensor Field - MotivationDynamic Sound Source Localization

Outline

Problems and ModelsTechnical ApproachReferences

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.


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))


Photo: courtesy of Michael Scanlon, ARL


Control over noisy, limited bandwidth, communication channels

Intelligent Servosystems Laboratory (ISL)


Dynamic Sound Source Localization - or why we need to move our head?

Biologically inspired algorithms


Barn Owl and RobotBarn Owl and Robot

Can we capture the barn owl’s auditory acuity in a binaural robot?


Sound Localization in Nature

• 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


Acoustic Cues for Localization

• 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


Place Theory (L. Jeffress)Place Theory (L. Jeffress)J. Comp. Physiol. & Psychol., (1948) 41:35-39J. 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)


Stereausis (S. Shamma et. al.)Stereausis (S. Shamma et. al.)J. Acoust. Soc. Am. (1989) 86:989-1006J. Acoust. Soc. Am. (1989) 86:989-1006

Ipsi-lateral cochlea

Characteristic frequency

SoundC

ha

ract

eris

tic

fre

qu

en

cyContara-lateralcochlea

AVCN

AV

CN

Ipsi- center contra-

lateral lateral

C kk +1

C kk

C kk -1

2jiij yxC jiij yxgC )0,max()(gwhere

Yj

CijXi

or


-45 deg (left)

Stereausis shifts from the main diagonal according to the source location.

45 deg (right)0 deg center

Incoming sound: a pure tone

Stereausis scheme (courtesy Shihab Shamma, UMd)


Lord Rayleigh and Binaural Perception • ILD and ITD both needed for azimuth (the concept of

HRTF). What about elevation?

1842-1919

See section 385 of The Theory of Sound1945 edition


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. Understandingsuch effects would matter in guiding robots towards acousticsource.


Coordinate Systems

zimuthal PolarElevationzimuth

Microphones at poles on horizontal plane


Static Solution

• Pressure field proportional to

• Does not depend on azimuthal angle (• Head Related Transfer Function (HRTF)• Numerical (e.g. FMP), and empirical methods for non-spherical heads


Feature Plane (cylinder) and Signatures

• 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


Symmetry of Static Localization

• 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 and Rotations

zimuthal PolarElevationzimuth


Breaking the Symmetry

• 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(-- thus elevation extracted from horizontal rotation!


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.


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


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


Plot on left shows distance functions for source at 15 deg and17.5 deg. Plot on right shows distance functions for source at72.5 deg and 75 deg.


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


New experiments in summer 2003 yielded raw data for furtherinvestigation of HRTF dependence on elevation. Front-backambiguity resolution via dynamic IPD-ILD algorithm implementedon robot. (See demo.) Plans to use soldier-helmet from ARL.

Photo: Courtesy of Michael Scanlon, ARL


Accomplishments

• 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


Implications and Applications

• Psychophysics: auditory displays, auditory component of virtual environments and hearing aids.

• Bio-mimetic active robot head

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).


Sound following


Front Back Demo

Without front-back distinction With front-back distinction

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