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Fast Bayesian Acoustic Localization

Fast Bayesian Acoustic Localization. Stan Birchfield Daniel Gillmor Quindi Corporation Palo Alto, California. Principle of Least Commitment. “Delay decisions as long as possible”. [Marr 1982; Russell & Norvig 1995; etc.]. Example:. Localization by Beamforming. mic 1 signal. delay.

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Fast Bayesian Acoustic Localization

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  1. Fast Bayesian Acoustic Localization Stan Birchfield Daniel Gillmor Quindi Corporation Palo Alto, California

  2. Principle of Least Commitment “Delay decisions as long as possible” [Marr 1982; Russell & Norvig 1995; etc.] Example:

  3. Localization by Beamforming mic 1 signal delay prefilter mic 2 signal delay prefilter q,f find peak sum energy mic 3 signal delay prefilter mic 4 signal delay prefilter [Duraiswami et al. 2001]

  4. Localization by Pair-wise TDE mic 1 signal decision is made early prefilter find peak correlate mic 2 signal prefilter q,f intersect (may be no intersection) mic 3 signal prefilter find peak correlate mic 4 signal prefilter [Brandstein et al. 1995; Brandstein & Silverman 1997; Wang & Chu 1997]

  5. Localization by Accumulated Correlation map to common coordinate system mic 1 signal prefilter correlate sampled locus mic 2 signal prefilter correlate final sampled locus … correlate q,f find peak sum correlate correlate temporal smoothing map to common coordinate system mic 3 signal prefilter correlate mic 4 signal prefilter decision is made after combining all the available evidence

  6. Bayesian Localization: A Unifying View ’ Bayesian Beamform Correlation (similarity) (energy)

  7. Comparison of V_C and V’_C ’ V V C C (sound generated at t ) (sound heard at t’ ) 0 0

  8. Our Microphone Array Geometry microphone sampled hemisphere d=15cm (Can handle arbitrary geometries)

  9. Results: Comparison of Algorithms f q SNR

  10. Results: Comparison of Algorithms Beamform Correlation Farfield [Birchfield & Gillmor 2001]

  11. Speed

  12. Multiple Uncorrelated Sound Sources + =

  13. Noise Localization Model background noise source

  14. Noise Localization Model -- Videos standard with noise localization model subtracted

  15. Conclusion • Bayesian localization • follows principle of least commitment • similar to beamforming (weights energy differently) • Accumulated correlation • close approximation to Bayesian and beamforming; similar to TDE • just as accurate, but 1000 times faster (for compact arrays) • handles multiple sound sources, including subtracting constant background noise source

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