Post on 16-Jan-2016
Audio BeamformingPresented by:
RAJI SUSAN MATHEWM.Tech Signal
Processing 3rd Semester
Reg No. 92111028
Internal Guide
Ms. Anjana Devi SAsst.ProfessorMEC
1
External Guide
Mr. AjithKumar P CArchitectTATA ELXSI Pvt Ltd.
Contents
Introduction Beamforming Adaptive LCMV Beamformer Wideband Constraints Source Tracking Results Conclusion
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Introduction
Beamforming - Spatial filtering Array of sensors Signal processing
Direct or block the radiation or the reception of signals in specified directions
Applications: Sonar radar siesmology radio astronomy
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Beamforming
Figure 1: Example of beamforming[14]
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Sensor arrays in Speech Processing
Comparatively newer area of research Use in hands free speech acquisition
hearing aids teleconferencing
Microphone arrays Set of microphones arranged in specific geometries
Beamformer Processes received signals to extract the desired signal
Based on the knowledge of location of the source Speech- Broadband signal
Narrowband techniques not useful in acoustic applications
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6
Beamforming
Data independent
Statistically optimum
Only the position of the desired source is known
Filter coefficients adjusted according to the array data
Desired signal strength is unknown
Difficult to estimate signal and noise covariance matrices
These limitations may overcome through application of linear
constraints
Linearly Constrained Minimum Variance Beamforming
Application of linear constraints to the weight
vector
Features
Constraint the response of beamformer
Minimizes output power
Preserves a unity gain at the look direction
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Adaptive LCMV Beamformer
Figure2: Adaptive LCMV Beamformer[6]
KknInsnr kkk ,...1),()()( Recorded signals represented as
= received signal
=desired signal
=interference SignalModelg.pptx
)(
)(
)(
nI
ns
nr
k
k
k
…..(1)
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C=constraint matrixf=constraint vector
)()( nXWny T
output
…..(2)
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fWCT
Under the constraint
WRWnyE XXT
WWminmin ])([ 2
…..(4)
…..(3)
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Find unit vector vTv ]cossinsincossin[
Steering delay calculation
Compute the delayCompute the delay
c
vmD
.
Constraint matrix formation
Weight computation
Filtering
LCMV Beamformer
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ArP.pptx
L
Ll cccC ....1 …..(5)
KL
Each weight =sum of weights in the corresponding vertical column
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Constraint matrix formation
Constraint matrix formation
Weight computation
Filtering
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1z 1z 1z 1z
1z 1z 1z 1z
1z 1z 1z 1z
y
Equivalence of array processor and tapped delay line
0f 1f 2f 3f Lf
Solution to the optimization problem
fCRCCRW xxT
xxopt111 ][
Assumes knowledge of Rxx
Rxx close to singular, matrix inverses may be incorrect
…..(6)
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Constraint matrix formation
Weight computationWeight computation
Filtering
TT
T
CCCCIP
fCCCF
FnX
nXnynWPnW
1
1
2
][
][
)(
)()()()1(
…..(7)
…..(8)
…..(10)
…..(9)
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FW )0(
Initial Weight vector
Weight update using NLMS Algorithm
NLMS.pptx
)()( nXWny T
output
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Constraint matrix formation
Weight computation
FilteringFiltering
…..(11)
Constraint modification
To provide some control over response locations To decrease the sensitivity to steering
inaccuracies To achieve constant bandwidth over the
frequency band of interest Additional constraints called Wideband
Constraints are added
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Wideband Constraints Pair of constraints
to fix the beamformer response in one particular direction at one frequency Fc
phaseshift
gaindesiredA
AAWsc TT
)]sin(),cos([],[ …..(12)
TKLKK
TKLKK
s
c
)sin(...)sin(......)sin(...)sin(
)cos(...)cos(......)cos(...)cos(
11
11
…..(13)
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Beamforming system
Bank of bandpass filters to split the input into frequency subbands
Each have its own adaptive beamformer
Constraints covering only its respective subband
A set of frequencies within the band of interest is chosen
A group of constraints at each of these frequency points is defined
Figure 3: Block diagram of beamforming system[6]
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Band Pass Filters Bank of bandpass filters to split the input into
frequency subbands (4 subbands, each1KHz wide) only one constraint frequency were chosen in the
lowest subband beamwidth at low frequencies is large
2 or 3 constraint frequency were chosen for the higher bands
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Source Tracking
Steering delays update
Wide band constraint
modification
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segment of the beamformer output output of the steering delay block
Compute cross correlation
the lag ( ) at which maxima
occurs
Compensation requiredsteering delays are correct
kpeakn
Lag=?
strg_up.pptx22
00
Steering delays update
=error in the kth steering delay Power varies between voiced and unvoiced
segments To determine whether given segment contains
strong desired signal
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k
Find the running maximum of peak value
Find the M-sample average value
Find the dynamic threshold value
)(max lp )(lpavg
)(lpTH
Contains desired signal Doesn’t contain desired signal
eqn.pptx
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)()( lplp TH )()( lplp TH?)( lp
Beamformer with tracking The lth segment contains desired signal if p(l)
is above the threshold value.
Figure 4:Beamformer with tracking[6]
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Constraint Modification for tracking
KLEUCJL
2
]|[
…..(14)
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constraint.pptx
Let the old set of steering delays used to generate one of the wideband constraints
the new delays are c’ and s’ are modified as
Kkk ,...,1,
kkk '
sccss
ssccc
'
'…..(15)
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Results: simulation using sine waveillustration of steering delay compensation
Figure 5: Before steering delay compensation
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Illustration of steering delay compensation
Figure 6: After steering delay compensation
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100 200 300 400 500 600 700-2
-1
0
1
no.of samples-->
ampl
itude
-->
microphone output
0 100 200 300 400 500 600 700 800
-1
0
1
no.of samples-->
ampl
itude
-->
beamformer output
Figure 7: time domain representation at various stages
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0 100 200 300 400 500 600 700
-1
0
1
no. of samples--->
ampl
itude
--->
desired signal
Figure 8: frequency response before beamforming
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0 2000 4000 6000 8000 10000 12000 14000 16000-20
-10
0
10
20
30
40
X: 1600Y: 35.83
frequency--->
mag
nitu
de--
>
frequency response
Figure 9: frequency response after beamforming
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0 2000 4000 6000 8000 10000 12000 14000 16000-20
-10
0
10
20
30
40
X: 1600Y: 17.74
mag
nitu
de--
->
frequency(Hz)-->
frequency response
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0 20 40 60 80 100 120 140 160 1800
100
200
300
400
500
600
700
800
900
1000Beamformer output vs look angle
Look angle(elevation)
Out
put
pow
er
Figure 10: Beamformer output vs look angle, with source at 900
Time domain plot of the desired speech signal at various stages
Figure 10:Time domain plot of the desired speech signal at various stages
0 0.5 1 1.5 2 2.5 3 3.5 4
x 104
-2
0
2
no.of samples-->
ampl
itude
-->
source
0 0.5 1 1.5 2 2.5 3 3.5 4
x 104
-2
0
2
no.of samples-->
ampl
itude
-->
microphone output
0 0.5 1 1.5 2 2.5 3 3.5 4
x 104
-2
0
2
no.of samples-->
ampl
itude
-->
beamformer output
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Desired signal𝜽=100,𝝓=20
Interfering signal𝜽=40,𝝓=20
Microphone output
Beamformer output
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Array of microphon
es
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 104
-2
0
2
no.of samples-->
ampl
itude
-->
source
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 104
-2
0
2
no.of samples-->
ampl
itude
-->
microphone output
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 104
-2
0
2
no.of samples-->
ampl
itude
-->
beamformer output
Figure 11:Time domain plot of the desired speech signal at various stages
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Desired signal𝜽=90,𝝓=30
Interfering signal𝜽=60,𝝓=40
Microphone output
Beamformer output
Array of microphon
es
Beamformer output vs look angle
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0 20 40 60 80 100 120 140 160 1800
10
20
30
40
50
60
70
80
90Beamformer output vs look angle
Look angle(elevation)
Out
put
pow
er
Figure 8: Beamformer output vs look angle, with source at 900
Conclusion
Beamformer forms the output signal as a weighted combination of data received at the array of sensors
Weights determine spatial filtering characteristics of the beamformer
Optimal improvement of speech quality in noisy environment can be achieved through spatial filtering
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Works completed Studied concepts of beamforming delay calculation of source and interference Calculation of steering delay for compensating
propagation delay Performed LCMV beamforming (without wideband
constraints) Works to be done LCMV beamforming with wideband constraints Source tracking
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Reference[1] Johnson , D H Dudgeon, “Array Signal Processing, Concepts and
Techniques”, Pentice Hall,1993[2] Otis Lamont Frost, “An Algorithm For Linearly ConstrainedAdaptive
Array Processing”, proceedings of the IEEE, Vol. 60, No. 8, August 1972
[3] K. M. Buckley, “Spatial/spectral filtering with linearly constrained minimum variance beamformers”, IEEE Transactions on Acoustics, Speech and SignalProcessing, vol. ASSP-35,No.3, pp. 249266, Mar. 1987.
[4] Barry D Van Veen, Kevin M Buckley, “Beamforming: A Versatile Approach to Spatial Filtering”, IEEE ASSP Magazine, April 1988
[5] Jacob Benesty, Jingdong Chen, Yiteng Huang and Jacck Dmochowski, “On Microphone Arrray Beamforming From a MIMO Acoustic Signal Processing Perspective”, IEEE Transactions on audio speech and language processing, vol.15, No.3, pp:1053-1065,March 2007
[6] Sergey Timofeev, Ahmad R. S. Bahai, and Pravin Varaiya, “Adaptive Acoustic Beamformer With Source Tracking Capabilities”, IEEE Transactions On Signal Processing, Vol. 56, No. 7, pp: 2812-2819, July 2008
[7]Olaf Jaeckel, “Strengths and weaknesses of calculating beamforming in the time domain”, Berlin beamforming Conference 2010
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Reference[8]Iain McCowen, "Robust Speech Recognition using Microphone
Arrays", PhD Thesis Queensland University of Technology, Australia 2001
[9]Athanassios Manikas and Christos Proukakis, "Modeling and Estimation ofAmbiguities in Linear Arrays, IEEE Transactions On Signal Processing, Vol.46, No. 8, August 1998
[10] D Ward, "Theory and Application of Broadband Frequency Invariant Beamforming", PhD thesis, Australian National University, July 1996
[11] R L Bouquin and G Faucon, "Using the coherence function for noise reduction", IEEE proceedings,vol.139, pp 276-280,June 1992
[12]S. Gazor, S. Aes, and Y. Grenier, "Robust adaptive beamforming via targettracking," IEEE Transactions on Acoustics, Speech, Signal Procesing., vol.44, pp. 15891593, Jun. 1996.
[13] Y. Kaneda and J. Ohga, "Adaptive microphone-array system for noise reduc-tion," IEEE Trans. Acoustics. Speech, Signal Processing,
vol. ASSP-34, no.6, pp. 13911400, Dec. 1986.[14] www.labbookpages.co.uk/audio/beamforming.html
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Thank You
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