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A Unifying Framework for Acoustic Localization

A Unifying Framework for Acoustic Localization. Stanley T. Birchfield Dept. of Electrical and Computer Engineering Clemson University Clemson, South Carolina USA. Acoustic Localization. distributed. compact. Problem: Use microphone signals to determine sound source location.

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A Unifying Framework for Acoustic Localization

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  1. A Unifying Framework for Acoustic Localization Stanley T. Birchfield Dept. of Electrical and Computer Engineering Clemson University Clemson, South Carolina USA

  2. Acoustic Localization distributed compact Problem: Use microphone signals to determine sound source location • Traditional solutions: • Delay-and-sum beamforming ! • Time-delay estimation (TDE) ! • Recent solutions: • Hemisphere sampling !! • Accumulated correlation !! • Bayesian ! • Zero-energy ! ! efficient! accurate

  3. Localization by Beamforming mic 1 signal makes decision late in pipeline (“principle of least commitment”) delay prefilter mic 2 signal delay prefilter q,f find peak sum energy mic 3 signal delay prefilter mic 4 signal delay prefilter delays (shifts) each signal for each candidate location [Silverman &Kirtman 1992; Duraiswami et al. 2001; Ward & Williamson, 2002] ! accurateNOT efficient

  4. Localization by Time-Delay Estimation (TDE) decision is made early mic 1 signal 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 cross-correlation computed once for each microphone pair [Brandstein et al. 1995; Brandstein & Silverman 1997; Wang & Chu 1997] ! efficient NOTaccurate

  5. Localization by Hemisphere Sampling 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 ! efficient ! accurate (but restricted to compact arrays) [Birchfield & Gillmor 2001]

  6. 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 ! efficient ! accurate [Birchfield & Gillmor 2002]

  7. accurate efficient Comparison Beamforming: energy similarity Bayesian: Zero energy: Acc corr: Hem samp: TDE:

  8. accurate efficient Unifying framework

  9. Integration limits Beamforming Bayesian Zero energy Accumulated correlationHemisphere sampling Time-delay estimation

  10. Results on compact array pan tilt without PHAT prefilter with PHAT prefilter

  11. Results on distributed array

  12. Computational efficiency Computing time per window (ms) (600x faster) (50x faster)

  13. Conclusion Traditional techniques of Beamforming and Time-delay estimation present tradeoff between Accuracy and efficiency The equations for Beamforming and Time-delay estimation are closely connected, leading to a unifying framework for acoustic localization algorithms Accumulated correlation is both Accurate and efficient, thus presenting an attractive alternative to beamforming with complicated search strategies

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