A Unifying Framework for Acoustic Localization

Stanley T. BirchfieldDept. of Electrical and Computer Engineering

Clemson UniversityClemson, South Carolina USA

Acoustic Localization

Problem: Use microphone signals to determine sound source location

Traditional solutions:1. Delay-and-sum beamforming !2. Time-delay estimation (TDE) !

compact

distributed

Recent solutions:3. Hemisphere sampling !!4. Accumulated correlation !!5. Bayesian !6. Zero-energy !

! efficient ! accurate

Localization by Beamforming

mic 1 signaldelay

mic 2 signal

prefilter

prefilter

mic 3 signal

find peak

mic 4 signal

prefilter

prefilter

sum

delay

delay

delay

[Silverman &Kirtman 1992; Duraiswami et al. 2001; Ward & Williamson, 2002]

energy

! accurate NOT efficient

makes decision late in pipeline(“principle of least commitment”)

delays (shifts) each signalfor each candidate location

Localization by Time-Delay Estimation (TDE)

mic 1 signal

correlatefind peakmic 2 signal

prefilter

prefilter

mic 3 signal

correlatefind peakmic 4 signal

prefilter

prefilter

intersect

(may be no intersection)

[Brandstein et al. 1995;

Brandstein & Silverman 1997;

Wang & Chu 1997]

! efficient NOT accurate

decision is made early

cross-correlation computed once for each microphone pair

Localization by Hemisphere Sampling

mic 1 signalcorrelate

map to common

coordinate system

sampled locus

sum

temporalsmoothing

mic 2 signal

prefilter

prefilter

mic 3 signalcorrelate

map to common

coordinate system

mic 4 signal

prefilter

prefilter

finalsampled

locus

correlate

correlate

correlate

correlate

… find peak

[Birchfield & Gillmor 2001]! efficient! accurate

(but restricted to compact arrays)

Localization by Accumulated Correlation

mic 1 signalcorrelate

map to common

coordinate system

sampled locus

sum

temporalsmoothing

mic 2 signal

prefilter

prefilter

mic 3 signalcorrelate

map to common

coordinate system

mic 4 signal

prefilter

prefilter

finalsampled

locus

correlate

correlate

correlate

correlate

… find peak

[Birchfield & Gillmor 2002]! efficient! accurate

ComparisonBeamforming:

Bayesian:

Zero energy:

Acc corr:

Hem samp:

TDE:

similarity energy

accurate

efficient

Unifying framework

efficient

accurate

Integration limits

BeamformingBayesianZero energy

Accumulated correlationHemisphere samplingTime-delay estimation

Results on compact array

pan

tilt

without PHAT prefilter with PHAT prefilter

Results on distributed array

Computational efficiency

0

1000

2000

3000

4000

5000

6000

7000

8000

Compact Distributed

Beamforming

Accumulatedcorrelation

Co

mp

uti

ng

tim

e p

er w

ind

ow

(m

s)

(600x faster) (50x faster)

ConclusionTraditional techniques of

Beamforming and Time-delay estimationpresent tradeoff betweenAccuracy and efficiency

The equations forBeamforming 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