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Position Calibration of Audio Sensors and Actuators in a Distributed Computing Platform Vikas C. Raykar | Igor Kozintsev | Rainer LienhartPowerPoint Presentation

Position Calibration of Audio Sensors and Actuators in a Distributed Computing Platform Vikas C. Raykar | Igor Kozintsev | Rainer Lienhart

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Position Calibration of Audio Sensors and Actuators in a Distributed Computing Platform Vikas C. Raykar | Igor Kozintsev | Rainer Lienhart University of Maryland, CollegePark | Intel Labs, Intel Corporation. Motivation.

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- PositionCalibration of Audio Sensors and Actuators
- in a Distributed ComputingPlatform
- Vikas C. Raykar | Igor Kozintsev | Rainer Lienhart
- University of Maryland, CollegePark | Intel Labs, Intel Corporation

Motivation

- Many multimedia applications are emerging which use multiple audio/video sensors and actuators.

Speakers

Microphones

Distributed Capture

Current Work

DistributedRendering

Cameras

Number

Crunching

Displays

Other Applications

What can you do with multiple microphones…

- Speaker localization and tracking.
- Beamforming or Spatial filtering.

X

Some Applications…

Speech Recognition

Hands free voice

communication

Novel Interactive audio

Visual Interfaces

Multichannel speech

Enhancement

Smart Conference

Rooms

Audio/Image Based

Rendering

Audio/Video

Surveillance

Speaker Localization

and tracking

MultiChannel echo

Cancellation

Source separation and

Dereverberation

Meeting Recording

More Motivation…

- Current work has focused on setting up all the sensors and actuators on a single dedicated computing platform.
- Dedicated infrastructure required in terms of the sensors, multi-channel interface cards and computing power.
On the other hand

- Computing devices such as laptops, PDAs, tablets, cellular phones,and camcorders have become pervasive.
- Audio/video sensors on different laptops can be used to form a distributed network of sensors.

Internal microphone

Common TIME and SPACE

- Put all the distributed audio/visual input/output capabilities of all the laptops into a common TIME and SPACE.
- For the common TIME see our poster.
Universal Synchronization Scheme for Distributed Audio-Video Capture on Heterogenous Computing Platforms R. Lienhart, I. Kozintsev and S. Wehr

- In this paper we deal with common SPACE i.e estimate the 3D positions of the sensors and actuators.
Why common SPACE

- Most array processing algorithms require that precise positions of microphones be known.
- Painful and Imprecise to do a manual measurement.

If we know the positions of speakers….

Y

If distances are not exact

If we have more speakers

Solve in the least square

sense

?

X

If positions of speakers unknown…

- Consider M Microphones and S speakers.
- What can we measure?

Distance between each speaker and all microphones.

Or Time Of Flight (TOF)

MxS TOF matrix

Assume TOF corrupted by Gaussian noise.

Can derive the ML estimate.

Calibration signal

Nonlinear Least Squares..

More formally can

derive the ML estimate

using a Gaussian

Noise model

Find the coordinates which minimizes this

and independent

ML is same as

Least squares

Maximum Likelihood (ML) Estimate..we can define a noise model

and derive the ML estimate i.e. maximize the likelihood ratio

Gaussian noise

Reference Coordinate system | Multiple Global minima

Reference Coordinate SystemPositive Y axis

Similarly in 3D

1.Fix origin (0,0,0)

2.Fix X axis

(x1,0,0)

3.Fix Y axis

(x2,y2,0)

4.Fix positive Z axis

x1,x2,y2>0

Origin

X axis

Which to choose? Later…

Multimedia/multistream applications

Operating system

I/O bus

Audio/video I/O devices

The journey of an audio sample..

Network

This laptop wants to play

a calibration signal on

the other laptop.

Play comand in software.

When will the sound be

actually played out from

The loudspeaker.

Time Origin

Signal Emitted by source j

t

Playback Started

Signal Received by microphone i

Capture Started

t

Joint Estimation..

Microphone and speaker

Coordinates

3(M+S)-6

Microphone Capture

Start Times

M -1

Assume tm_1=0

Totally

4M+4S-7 parameters to estimates

MS observations

Can reduce the number of parameters

Speaker Emission

Start Times

S

Levenberg Marquadrat

method

Function of a large number of parameters

Unless we have a good initial guess may not converge

to the minima.

Approximate initial guess required.

Closed form Solution..

Say if we are given all pairwise distances between N points can we get the coordinates.

Classical Metric Multi Dimensional Scaling

dot product matrix

Symmetric positive definite

rank 3

Given B can you get X ?....Singular Value Decomposition

Same as

Principal component Analysis

But we can measure

Only the pairwise distance matrix

1. We do not have

the complete

pairwise distances

2. Measured distances

Include the

effect of lack of

synchronization

UNKNOWN

UNKNOWN

Finally the complete algorithm…

Approximation

TOF matrix

Clustering

Approx

ts

Approx

Distance matrix

between GPCs

Dot product matrix

Approx

tm

Dimension and

coordinate system

MDS to get approx

GPC locations

TDOA based

Nonlinear

minimization

perturb

Microphone and speaker

locations

tm

Approx. microphone

and speaker

locations

- The performance of our algorithm depends on
- Noise Variance in the estimated distances.
- Number of microphones and speakers.
- Microphone and speaker geometry

- One way to study the dependence is to do a lot of monte carlo simulations.
- Or given a noise model can derive bounds on how worst can our algortihm perform.
- The Cramer Rao bound.

- Gives the lower bound on the variance of any unbiased estimator.
- Does not depends on the estimator. Just the data and the noise model.
- Basically tells us to what extent the noise limits our performance i.e. you cannot get a variance lesser than the CR bound.

Rank Deficit..remove the

Known parameters

Jacobian

Number of sensors matter… estimator.

Number of sensors matter… estimator.

Geometry also matters… estimator.

Geometry also matters… estimator.

X estimator.

Room Height = 2.03 m

Speaker 3

Mic 3

Mic 4

Room Length = 4.22 m

Speaker 2

Speaker 4

Mic 2

Mic 1

Speaker 1

Z

Room Width = 2.55 m

Synchronized setup | bias 0.08 cm sigma 3.8 cm

Experimental results using real data estimator.

Summary estimator.

- General purpose computers can be used for distributed array processing
- It is possible to define common time and space for a network of distributed sensors and actuators.
- For more information please see our two papers or contact [email protected]
- [email protected]
- Let us know if you will be interested in testing/using out time and space synchronization software for developing distributed algorithms on GPCs (available in January 2004)

Thank You ! | Questions ? estimator.

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