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A Unifying Framework for Acoustic Localization

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Presentation on theme: "A Unifying Framework for Acoustic Localization"— Presentation transcript:

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”) prefilter delay mic 2 signal prefilter delay find peak q,f sum energy mic 3 signal prefilter delay mic 4 signal prefilter delay delays (shifts) each signal for each candidate location [Silverman &Kirtman 1992; Duraiswami et al. 2001; Ward & Williamson, 2002] ! accurate NOT efficient

4 Localization by Time-Delay Estimation (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 cross-correlation computed once for each microphone pair [Brandstein et al. 1995; Brandstein & Silverman 1997; Wang & Chu 1997] ! efficient NOT accurate

5 Localization by Hemisphere Sampling
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 ! 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 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 ! efficient ! accurate [Birchfield & Gillmor 2002]

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

8 Unifying framework accurate efficient

9 Integration limits Beamforming Bayesian Zero energy
Accumulated correlation Hemisphere 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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