Acoustic Localization by Interaural Level Difference Rajitha Gangishetty

7/14/2005Acoustic Localization by ILD2 Acoustic Localization compact microphone array   sound source d Acoustic Localization: Determining the location of a sound source by comparing the signals received by an array of microphones. Issues: reverberation noise

7/14/2005Acoustic Localization by ILD3 Overview What is Interaural Level Difference (ILD)? ILD Formulation ILD Localization Simulation Results Conclusion and Future Work

7/14/2005Acoustic Localization by ILD4 Techniques Interaural time difference (ITD): relative time shift ITD ILD sound source microphones Interaural level difference (ILD): relative energy level All previous methods (TDE, beamforming, etc.) use ITD alone.

7/14/2005Acoustic Localization by ILD5 Previous Work Time Delay Estimation [M. S. Brandstein, H. F. Silverman, ICASSP 1997; P. Svaizer, M. Matassoni, M. Omologo, ICASSP 1997] Beamforming J. L. Flanagan, J.D. Johnston, R. Zahn, JASA 1985; R. Duraiswami, D. Zotkin, L.Davis, ICASSP 2001] Accumulated Correlation [Stanley T. Birchfield, EUSIPCO 2004] Microphone arrays [Michael S. Brandstein, Harvey F. Silverman, ICASSP 1995; P. Svaizer, M. Matassoni, M. Omologo, ICASSP 1997] Hilbert Envelope Approach [David R. Fischell, Cecil H. Coker, ICASSP 1984]

7/14/2005Acoustic Localization by ILD6 A sneak peek at the results Likelihood plots, Estimation error, Comparison of different approaches likelihood function computed by horizontal and vertical microphone pairs contour plots of likelihood functions (overlaid and combined) microphonestrue location

7/14/2005Acoustic Localization by ILD7 ILD Formulation N microphones and a source signal s(t) Signal received by the i th microphone d i = distance from source to the i th microphone = additive white Gaussian noise Energy received by i th microphone

7/14/2005Acoustic Localization by ILD8 ILD Formulation For 2 mics the relation between energies and distances is Given E1 and E2 the sound source lies on a locus of points (a circle or line) described by where,

7/14/2005Acoustic Localization by ILD9 ILD Formulation For E1 ≠ E2 the equation becomes which is a circle with center and radius In 3D the circle becomes a sphere For E1= E2 the equation becomes which becomes a plane in 3D

7/14/2005Acoustic Localization by ILD10 Isocontours for 10log(delta E)

7/14/2005Acoustic Localization by ILD11 ILD Localization With only two microphones source is constrained to lie on a curve The microphones cannot pinpoint the sound source location We use multiple microphone pairs The intersection of the curves yield the sound source location Why multiple microphone pairs?

7/14/2005Acoustic Localization by ILD12 Combined Likelihood Approach Then the estimate for the energy ratio at candidate location is Then the estimate for the energy ratio at candidate location is Define the energy ratio as Define the energy ratio as where is the location of the ith microphone is treated as a Gaussian is treated as a Gaussian random variable random variable Joint probability from multiple microphone Joint probability from multiple microphone pairs is computed by combining the pairs is computed by combining the individual log likelihoods individual log likelihoods Localize sound source by computing likelihood at a number of candidate locations:

7/14/2005Acoustic Localization by ILD13 Hilbert Transform The Hilbert transform returns a complex sequence, from a real data sequence. The complex signal x = x r + i*x i has a real part, x r, which is the original data, and an imaginary part, x i, which contains the Hilbert transform. The imaginary part is a version of the original real sequence with a 90° phase shift. Sines are therefore transformed to cosines and vice versa.

7/14/2005Acoustic Localization by ILD14 Hilbert Transformer x r [n] Complex Signal x[n] Hilbert Transformer h[n] x r [n] x i [n] -j, 0<w<pi j, -pi<w<0 H(e jw ) = where ‘w’ is the angular frequency The Hilbert transformed series has the same amplitude and frequency content as the original real data and includes phase information that depends on the phase of the original data. In Frequency domain, X i (e jw ) = H(e jw )X r (e jw )

7/14/2005Acoustic Localization by ILD15 Hilbert Envelope Approach All-pass filter circuit produces two signals with equal amplitude but 90 degrees out of phase. Square root of the sum of squares is taken. (90 o ) (90 o ) 2 (0 o ) 2

7/14/2005Acoustic Localization by ILD16 Simulated Room

7/14/2005Acoustic Localization by ILD17 Simulation Results The algorithm Accurately estimates the angle to the sound source in some scenarios Exhibits bias toward far locations (unable to reliably estimate the distance to the sound source) Is sensitive to noise and reverberation

7/14/2005Acoustic Localization by ILD18 Results of delta E Estimation The estimation is highly dependent upon the sound source location amount of reverberation amount of noise size of the room relative positions of source and microphones

7/14/2005Acoustic Localization by ILD19 Likelihood plots 5x5 m room, theta = 45 deg, no noise, no reverberation, d = 2m

7/14/2005Acoustic Localization by ILD20 Likelihood plots 5x5 m room, theta = 90 deg, SNR = 0db, reflection coefficient = 9, d = 2m

7/14/2005Acoustic Localization by ILD21 Likelihood plots 5x5 m room, theta = 0 deg, SNR = 0db, reflection coefficient = 9, d = 1m

7/14/2005Acoustic Localization by ILD22 Likelihood plots 10x10 m room, theta = 0 deg, SNR = 0db, reflection coefficient = 9, d = 1m angle error = 6.5 degrees

7/14/2005Acoustic Localization by ILD23 Likelihood plots 5x5 m room, theta = 36 deg, SNR = 0db, reflection coefficient = 9, d = 2m angle error = 9 degrees

7/14/2005Acoustic Localization by ILD24 Angle Errors in a 5x5 m room d = 1m, only noised = 2m, only noise d = 1m, only reverberationd = 2m, only reverberation 0.7 = solid line, blue 0.8 = dotted, red 0.9 = dashed, green 20 dB = solid line, blue 10 dB = dotted, red 0 dB = dashed, green

7/14/2005Acoustic Localization by ILD25 Angle error in degrees for the 10x10 m room when the source is at a distance of 1m Angle error in degrees for the 5x5 m room when the source is at a distance of 1m

7/14/2005Acoustic Localization by ILD26 Angle error in degrees for the 10x10 m room when the source is at a distance of 2m Angle error in degrees for the 5x5 m room when the source is at a distance of 2m

7/14/2005Acoustic Localization by ILD27 Comparison of errors with the Hilbert Envelope Approach in a 5x5 m room dB10 dB0 dB Without Hilbert = solid line, blue Matlab Hilbert = dotted line, red Kaiser Hilbert = dashed line, green Reflection coefficient

7/14/2005Acoustic Localization by ILD28 Comparison of errors with the Hilbert Envelope Approach in a 10x10 m room dB10 dB0 dB Without Hilbert = solid line, blue Matlab Hilbert = dotted line, red Kaiser Hilbert = dashed line, green Reflection coefficient

7/14/2005Acoustic Localization by ILD29 Likelihood plots without Hilbert Envelope angle error = 27 degrees 5x5 m room, theta = 18 deg, SNR = 0db, reflection coefficient = 9, d = 2m

7/14/2005Acoustic Localization by ILD30 Frames approach Signal divided into 50 frames Frame size = 92.8ms 50% overlap in each frame 5x5 m room, theta = 18 deg, SNR = 0db, reflection coefficient = 9, d = 2m (left), d = 1m (right) Mean error = 15 deg Std Dev = 11 deg Mean error = 7 deg Std Dev = 6 deg

7/14/2005Acoustic Localization by ILD31 Conclusion and Future Work ILD is an important cue for acoustic localizationILD is an important cue for acoustic localization Preliminary results indicate potential for ILD (Algorithm yields accurate results for several configurations, even with noise and reverberation) Preliminary results indicate potential for ILD (Algorithm yields accurate results for several configurations, even with noise and reverberation) Future work: Future work: Investigate issues (e.g., bias toward distant locations, sensitivity to reverberation) Investigate issues (e.g., bias toward distant locations, sensitivity to reverberation) Experiment in real environments Experiment in real environments Investigate ILDs in the case of occlusion Investigate ILDs in the case of occlusion Combine with ITD to yield more robust results Combine with ITD to yield more robust results

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