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Digital Audio Signal Processing Lecture-2: Microphone Array Processing - Fixed Beamforming -. Marc Moonen Dept. E.E./ESAT-STADIUS, KU Leuven [email protected] homes.esat.kuleuven.be /~ moonen /. Overview. Introduction & beamforming basics Data model & definitions - PowerPoint PPT Presentation
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Digital Audio Signal Processing
Lecture-2: Microphone Array Processing
- Fixed Beamforming -
Marc Moonen Dept. E.E./ESAT-STADIUS, KU Leuven
homes.esat.kuleuven.be/~moonen/
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 2
Overview
• Introduction & beamforming basics– Data model & definitions
• Fixed beamforming– Filter-and-sum beamformer design – Matched filtering
• White noise gain maximization• Ex: Delay-and-sum beamforming
– Superdirective beamforming • Directivity maximization
– Directional microphones (delay-and-subtract)
• Lecture-3: Adaptive Beamforming
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 3
Introduction
• Directivity pattern of a microphone– A microphone is characterized by a `directivity pattern’
which specifies the gain (& phase shift) that the
microphone gives to a signal coming from
a certain direction (i.e. `angle-of-arrival’) – In general the directivity pattern is also a function of frequency (ω) – In a 3D scenario `angle-of-arrival’
is azimuth + elevation angle– Will consider only 2D scenarios for
simplicity, with one angle-of arrival (θ),
hence directivity pattern is H(ω,θ) – Directivity pattern is fixed and defined
by physical microphone design
|H(ω,θ)| for 1 frequency
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 4
Introduction
• By weighting or filtering (=freq.dependent weighting) and then summing signals from different microphones, a (software controlled) `virtual’ directivity pattern (=weigthed sum of individual patterns) can be produced
This assumes all microphones receive the same signals (so are all in the same position). However…
M
mmm HFH
1virtual ),().(),(
01000
20003000
045
90135
180
0
0.5
1
Angle (deg)
+:
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 5
Introduction
• However, an additional aspect is that in a microphone array different
microphones are in different positions/locations, hence also receive
different signals
• Example : uniform linear array i.e. microphones placed on a line & uniform inter-micr. distances (d) & ideal micr. characteristics (p.8) For a far-field source signal (plane waveforms), each microphone receives the same signal, up to an angle-dependent delay fs=sampling rate c=propagation speed
][][ 1 mm kyky
sm
m fc
d cos)(
dmdm )1(
+:
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 6
Introduction
• Beamforming
= `spatial filtering’ based on
microphone characteristics
(directivity patterns)
AND
microphone array configuration
(`spatial sampling’)
+:
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 7
Introduction
• Background/history: ideas borrowed from antenna array design/processing for RADAR & (later) wireless communications.
• Microphone array processing considerably more difficult than antenna array processing: – narrowband radio signals versus broadband audio signals– far-field (plane wavefronts) versus near-field (spherical wavefronts)– pure-delay environment versus multi-path environment
• Classification:– fixed beamforming: data-independent, fixed filters Fm
e.g. delay-and-sum, filter-and-sum– adaptive beamforming: data-dependent adaptive filters Fm
e.g. LCMV-beamformer, Generalized Sidelobe canceler
• Applications: voice controlled systems (e.g. Xbox Kinect), speech communication systems, hearing aids,…
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 8
Beamforming basics
Data model: source signal in far-field (see p.12 for near-field)
• Microphone signals are filtered versions of source signal S() at angle
• Stack all microphone signals in a vector
d is `steering vector’
• Output signal after `filter-and-sum’ is
TjM
j MeHeH )()(1 ).,(...).,(),( 1 d
)()}.,().({),().(),().(),(1
* SYFZ HHM
mmm dFYF
H instead of T for convenience (**)
)( ..),(),(
shift phase dep.-pos.
)(
pattern dir.
SeHY mjmm
)().,(),( SdY
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 9
Data Model: source signal in far-field
• If all microphones have the same directivity pattern Ho(ω,θ), steering vector can be factored as…
• Will often consider arrays with
ideal omni-directional microphones : Ho(ω,θ)=1 Example : uniform linear array, see p.5
Beamforming basics
)().,(),( SdY
positions spatial
)()(
pattern dir.
0 ...1.),(),( 2Tjj MeeH d
microphone-1 is used as a reference (=arbitrary)
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 10
Beamforming basics
Definitions (1) :• In a linear array (p.5) : =90o=broadside direction = 0o =end-fire direction• Array directivity pattern (compare to p.3)
= `transfer function’ for source at angle ( -π<< π )
• Steering direction = angle with maximum amplification (for 1 freq.)
• Beamwidth (BW)
= region around max with (e.g.) amplification > -3dB (for 1 freq.)
),().()(
),(),(
dFH
S
ZH
),(maxarg)(max H
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 11
Beamforming basics
Data model: source signal + noise• Microphone signals are corrupted by additive noise
• Define noise correlation matrix as
• Will assume noise field is homogeneous, i.e. all diagonal elements of noise correlation matrix are equal :
• Then noise coherence matrix is
TMNNN )(...)()()( 21 N
})().({)( Hnoise E NNΦ
iΦΦ noiseii , )()(
1....
....
....1
)(.)(
1)(
noise
noisenoise ΦΓ
)()().,(),( NdY S
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 12
Beamforming basics
Definitions (2) :• Array Gain =improvement in SNR for source at angle ( -π<< π )
• White Noise Gain =array gain for spatially uncorrelated noise (=white)
(e.g. sensor noise)
ps: often used as a measure for robustness• Directivity =array gain for diffuse noise (=coming from all directions)
DI and WNG evaluated at max is often used as a performance criterion
c
ddf ijsdiffuseij
)(sinc)(
(ignore this formula))(.).(
),().(),(
2
FF
dFdiffusenoise
H
H
DI
)().(
),().(),(
2
FF
dFH
H
WNG Iwhite
noise
|signal transfer function|^2
|noise transfer function|^2)().().(
),().(),(
2
FΓF
dF
noiseH
H
input
output
SNR
SNRG
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 13
• Far-field assumptions not valid for sources close to microphone array– spherical wavefronts instead of planar waveforms– include attenuation of signals– 2 coordinates ,r (=position q) instead of 1 coordinate (in 2D case)
• Different steering vector (e.g. with Hm(ω,θ)=1 m=1..M) :
PS: Near-field beamforming
TjM
jj Meaeaea )()(2
)(1
21),( qqqqd ),( d
s
mref
m fc
pqpqq
)(
with q position of source pref position of reference microphone pm position of mth microphone
m
ref
mapq
pq
e
e=1 (3D)…2 (2D)
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 14
PS: Multipath propagation
• In a multipath scenario, acoustic waves are reflected against walls, objects, etc..
• Every reflection may be treated as a separate source (near-field or far-field)
• A more practical data model is:
with q position of source and Hm(ω,q), complete transfer function from source position to m-the microphone (incl. micr. characteristic, position, and multipath propagation)
`Beamforming’ aspect vanishes here, see also Lecture-3
(`multi-channel noise reduction’)
)()().,(),( NqdqY S
TMHHH ),(...),(),(),( 21 qqqqd
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 15
Overview
• Introduction & beamforming basics – Data model & definitions
• Fixed beamforming– Filter-and-sum beamformer design – Matched filtering
• White noise gain maximization• Ex: Delay-and-sum beamforming
– Superdirective beamforming • Directivity maximization
– Directional microphones (delay-and-subtract)
• Lecture-3: Adaptive beamforming
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 16
Filter-and-sum beamformer design
• Basic: procedure based on page 9
Array directivity pattern to be matched to given (desired) pattern over frequency/angle range of interest
• Non-linear optimization for FIR filter design (=ignore phase response)
• Quadratic optimization for FIR filter design (=co-design phase response)
),().()(
),(),(
dFH
S
ZH
),( dH
1
0,1 .)( , )(...)()(
N
n
jnnmm
TM efFFF F
ddHH dNnMmf nm ),(),(min
2
1..0,..1,,
ddHH dNnMmf nm ),(),(min
2
1..0,..1,,
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 17
Filter-and-sum beamformer design
• Quadratic optimization for FIR filter design (continued)
With
optimal solution is
ddHH dNnMmf nm ),(),(min
2
1..0,..1,,
),(
).1(
.0
1
11,0,
),(.)(.),(.)(...)(),().(),(
... , ...
d
dfddF
ffffNMxM
jN
j
MxMTH
MHH
TTM
TTNmmm
e
e
IFFH
ff
ddHdd d
Hoptimal ),().,( , ),().,( , . *1 dpddQpQf
Kronecker product
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 18
Filter-and-sum beamformer design
• Design example
M=8Logarithmic arrayN=50fs=8 kHz
01000
20003000
045
90135
180
0
0.5
1
Angle (deg)
Frequency (Hz)
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 19
Overview
• Introduction & beamforming basics – Data model & definitions
• Fixed beamforming– Filter-and-sum beamformer design – Matched filtering
• White noise gain maximization• Ex: Delay-and-sum beamforming
– Superdirective beamforming • Directivity maximization
– Directional microphones (delay-and-subtract)
• Lecture-3: Adaptive beamforming
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 20
Matched filtering : WNG maximization
• Basic: procedure based on page 11
• Maximize White Noise Gain (WNG) for given steering angle ψ
• A priori knowledge/assumptions:– angle-of-arrival ψ of desired signal + corresponding steering vector– noise scenario = white
})().(
),().(arg{max)},(arg{max)(
2
)()(MF
FF
dFF FF H
H
WNG
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 21
Matched filtering : WNG maximization
• Maximization in
is equivalent to minimization of noise output power (under
white input noise), subject to unit response for steering angle (**)
• Optimal solution (`matched filter’) is
• [FIR approximation]
),(.),(
1)( 2
MF
dd
F
1),().( s.t. ),().(min )( dFFFFHH
})().(
),().(arg{max)(
2
)(MF
FF
dFF F H
H
dNnMmf nm
2MF1..0,..1, )()(min
,FF
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 22
Matched filtering example: Delay-and-sum
• Basic: Microphone signals are delayed and then summed together
• Fractional delays implemented with truncated interpolation filters (=FIR)
• Consider array with ideal omni-directional micr’s
Then array can be steered to angle :
Hence (for ideal omni-dir. micr.’s) this is matched filter solution
1),( H
d
cos)1( dm
d
2
m
1
M
1
M
eF
mj
m
)(
M
mmm ky
Mkz
1
][.1
][
Tjj Mee )()( ...1),( 2 d )(mm M
),()(
dF
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 23
Matched filtering example: Delay-and-sum
• Array directivity pattern H(ω,θ):
=destructive interference
=constructive interference• White noise gain :
(independent of ω)
For ideal omni-dir. micr. array, delay-and-sum beamformer provides
WNG equal to M for all freqs. (in the direction of steering angle ψ).
)( 1),(
1),(
),().,(1
),(
max
H
HM
H H dd
MWNGH
H
..)().(
),().(),(
2
FF
dF
ideal omni-dir. micr.’s
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 24
02000
40006000
8000 045
90135
180
0.2
0.4
0.6
0.8
1
Angle (deg)Frequency (Hz)
• Array directivity pattern H(,) for uniform linear array:
H(,) has sinc-like shape and is frequency-dependent
Matched filtering example: Delay-and-sum
)2/sin(
)2/sin(
),(
2/
2/1
)cos(cos)1(
j
jM
M
m
fc
dmj
e
Me
eHs
-20
-10
0
90
270
180 0
Spatial directivity pattern for f=5000 Hz
M=5 microphones
d=3 cm inter-microphone distance
=60 steering angle
fs=16 kHz sampling frequency
=endfire
1),( H
=60wavelength=4cm
ideal omni-dir. micr.’s
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 25
0
2000
4000
6000
8000 050
100150
0.20.40.60.8
1
Angle (deg)
Matched filtering example: Delay-and-sum
For an ambiguity, called spatial aliasing, occurs.
This is analogous to time-domain aliasing where now the spatial sampling (=d) is too large. Aliasing does not occur (for any ) if
M=5, =60, fs=16 kHz, d=8 cm
)cos1.(
d
cf
2.2min
max
f
c
f
cd
s
)cos1.(.. and 0for occurs 2 then
2 if 3)
)cos1.(.. and for occurs 2 then
2 if 2)
) all(for for 0 )1
integer for 2 1),(
Details...
d
cfπ
d
cfπ
pπ.pγiffH
ideal omni-dir. micr.’s
)cos1.(
d
cf
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 26
Matched filtering example: Delay-and-sum
• Beamwidth for a uniform linear array:
hence large dependence on # microphones, distance (compare p.22 & 23) and frequency (e.g. BW infinitely large at DC)
• Array topologies:– Uniformly spaced arrays– Nested (logarithmic) arrays (small d for high , large d for small )– 2D- (planar) / 3D-arrays
with e.g. =1/sqrt(2) (-3 dB)
d
2d
4d
sec
)1(96
dMcBW
ideal omni-dir. micr.’s
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 27
Overview
• Introduction & beamforming basics – Data model & definitions
• Fixed beamforming– Filter-and-sum beamformer design – Matched filtering
• White noise gain maximization• Ex: Delay-and-sum beamforming
– Superdirective beamforming • Directivity maximization
– Directional microphones (delay-and-subtract)
• Lecture-3: Adaptive beamforming
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 28
Super-directive beamforming : DI maximization
• Basic: procedure based on page 11
• Maximize Directivity (DI) for given steering angle ψ
• A priori knowledge/assumptions:– angle-of-arrival ψ of desired signal + corresponding steering vector– noise scenario = diffuse
})(.).(
),().(arg{max)},(arg{max)(
2
)()(SD
FF
dFF FF diffuse
noiseH
H
DI
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 29
Super-directive beamforming : DI maximization
• Maximization in
is equivalent to minimization of noise output power (under
diffuse input noise), subject to unit response for steering angle (**)
• Optimal solution is
• [FIR approximation]
})(.).(
),().(arg{max)(
2
)(SD
FF
dFF F diffuse
noiseH
H
1),().( s.t. ),().().(min )( dFFFFHdiffuse
noiseH
),(.)}(.{1
)( 1
),(.1)}(.{),(
SD
dFdd
diffusenoisediffuse
noiseH
dNnMmf nm
2SD1..0,..1, )()(min
,FF
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 30
• Directivity patterns for end-fire steering (ψ=0):
Superdirective beamformer has highest DI, but very poor WNG (at low frequencies, where diffuse noise coherence matrix becomes ill-conditioned)
hence problems with robustness (e.g. sensor noise) !
Super-directive beamforming : DI maximization
M 2
-20
-10
0
90
270
180 0
Superdirective beamformer (f=3000 Hz)
-20
-10
0
90
270
180 0
Delay-and-sum beamformer (f=3000 Hz)
M=5 d=3 cmfs=16 kHz
Maximum directivity=M.M obtained for end-fire steering and for frequency->0 (no proof)
ideal omni-dir. micr.’s
0 2000 4000 6000 80000
5
10
15
20
25
Frequency (Hz)
Dir
ecti
vit
y (
lin
ear)
SuperdirectiveDelay-and-sum
0 2000 4000 6000 8000-60
-50
-40
-30
-20
-10
0
10
Frequency (Hz)
Whit
e n
ois
e g
ain
(d
B)
SuperdirectiveDelay-and-sum
WNG=5
PS: diffuse noise =white noise for high frequencies
DI=5
DI=25
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 31
Overview
• Introduction & beamforming basics – Data model & definitions
• Fixed beamforming– Filter-and-sum beamformer design – Matched filtering
• White noise gain maximization• Ex: Delay-and-sum beamforming
– Superdirective beamforming • Directivity maximization
– Directional microphones (delay-and-subtract)
• Lecture-3: Adaptive beamforming
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 32
• First-order differential microphone = directional microphone 2 closely spaced microphones, where one microphone is delayed (=hardware) and whose outputs are then subtracted from each other
d
+
_
)cos
(1),( c
dj
eH
Differential microphones : Delay-and-subtract
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 33
• First-order differential microphone = directional microphone 2 closely spaced microphones, where one microphone is delayed (=hardware) and whose outputs are then subtracted from each other
• Array directivity pattern:
– First-order high-pass frequency dependence– P() = freq.independent (!) directional response– 0 1 1 : P() is scaled cosine, shifted up with 1
such that max = 0o (=end-fire) and P(max )=1
d
+
_
)cos
(1),( c
dj
eH
d/c <<, <<
cd /1
cos)1()( 11 P
dependence anglepass-high
)()..()cos.(),( Pc
d
c
dH
Differential microphones : Delay-and-subtract
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 34
Differential microphones : Delay-and-subtract
• Types: dipole, cardioid, hypercardioid, supercardioid (HJ84)Dipole: 1= 0 (=0)
zero at 90o DI = 4.8 dB
Cardioid: 1= 0.5
zero at 180o DI = 4.8 dB
=endfire=endfire
=broadside =broadside
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 35
Differential microphones : Delay-and-subtract
Hypercardioid: 1= 0.25
zero at 109o highest DI=6.0 dB
Supercardioid: 1=
zero at 125o, DI=5.7 dB highest front-to-back ratio
35.02/)13(
=endfire=endfire
Digital Audio Signal Processing Version 2014-2015 Lecture-2: Microphone Array Processing p. 36
Overview
• Introduction & beamforming basics – Data model & definitions
• Fixed beamforming– Filter-and-sum beamformer design – Matched filtering
• White noise gain maximization• Ex: Delay-and-sum beamforming
– Superdirective beamforming • Directivity maximization
– Directional microphones (delay-and-subtract)
• Lecture-3: Adaptive beamforming