Fast Bayesian Acoustic Localization

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Presentation transcript:

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

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]

Localization by Accumulated Correlation map to common coordinate system mic 1 signal prefilter correlate mic 2 signal prefilter sampled locus correlate … final sampled locus correlate q,f sum find peak 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

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

Comparison of V_C and V’_C (sound generated at t ) (sound heard at t’ )

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

Results: Comparison of Algorithms q SNR

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

Speed Algorithm Running time (ms) per 55 ms window Farfield 5.5 Correlation 6.2 Beamform 4160.1 Bayesian 3968.5

Multiple Uncorrelated Sound Sources + =

Noise Localization Model background noise source

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

Conclusion Bayesian localization Accumulated correlation 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