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Multi-microphone noise reduction Multi-microphone noise reduction
and dereverberation techniques and dereverberation techniques
for speech applicationsfor speech applications
Simon Doclo
Dept. of Electrical Engineering, KU Leuven, Belgium
8 July 2003
22
OverviewOverview
• Introduction
• Basic principles
• Robust broadband beamforming
• Multi-microphone optimal filtering
• Acoustic transfer function estimation and dereverberation
• Conclusion and further research
33
OverviewOverview
• Introduction
� Motivation and applications
� Problem statement
� Contributions
• Basic principles
• Robust broadband beamforming
• Multi-microphone optimal filtering
• Acoustic transfer function estimation and dereverberation
• Conclusion and further research
44
• Speech acquisition in an adverse acoustic environment
MotivationMotivation
• Speech communication applications: hands-free mobiletelephony, voice-controlled systems, hearing aids
Background noise:- fan, radio- other speakers- generally unknown
Reverberation- reflections of signal against walls, objects
• Poor signal quality
• Speech intelligibility and speech recognition
Introduction -Motivation -Problem statement
-Contributions
Basic principles
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation anddereverberation
Conclusion
55
Signal enhancement
ObjectivesObjectives
• Signal enhancement techniques:� Noise reduction : reduce amount of background
noise without distorting speech signal� Dereverberation : reduce effect of signal reflections
� Combined noise reduction and dereverberation
• Acoustic source localisation: video camera or spotlight
Introduction -Motivation -Problem statement
-Contributions
Basic principles
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation anddereverberation
Conclusion
66
• Video-conferencing:� Microphone array for source localisation :
– point camera towards active speaker– signal enhancement by steering of microphone array
ApplicationsApplications
• Hands-free mobile telephony:
� Most important application from economic point of view
� Hands-free car kit mandatory in many countries
� Most current systems: 1 directional microphone
Introduction -Motivation -Problem statement
-Contributions
Basic principles
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation anddereverberation
Conclusion
77
• Hearing aids and cochlear implants:� most hearing impaired suffer from perceptual hearing
loss amplification
reduction of noise wrt useful speech signal
ApplicationsApplications
• Voice-controlled systems:� domotic systems, consumer electronics (HiFi, PC software)� added value only when speech recognition system
performs reliably under all circumstances� signal enhancement as pre-processing step
� multiple microphones + DSP in hearing aid� current systems: simple beamforming � robustness important due to small inter-microphone distance
Introduction -Motivation -Problem statement
-Contributions
Basic principles
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
88
Algorithmic requirementsAlgorithmic requirements
• ‘Blind’ techniques: unknown noise sources and acoustic environment
• Adaptive: time-variant signals and acoustic environment
• Robustness:
� Microphone characteristics (gain, phase, position)
� Other deviations from assumed signal model
(look direction error, VAD)
• Integration of different enhancement techniques
• Computational complexity
Introduction -Motivation -Problem statement
-Contributions
Basic principles
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
99
Problem statementProblem statement
• Problem of existing techniques:
� Single-microphone techniques: very limited performance multi-microphone techniques: exploit spatial
information multiple microphones required for source localisation
� A-priori assumptions about position of signal sources and microphone array: large sensitivity to deviations improve robustness (and performance)
� Assumption of spatio-temporally white noise extension to coloured noise
Development of multi-microphone noise reduction and dereverberation
techniques with better performance and robustness
for coloured noise scenarios
Introduction -Motivation -Problem statement
-Contributions
Basic principles
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
State-of-the-art and State-of-the-art and
contributionscontributions
1010
Single-microphone techniques
– spectral subtraction [Boll 79, Ephraim 85, Xie 96]
•Signal-independent transformation
•Residual noise problem
– subspace-based [Dendrinos 91, Ephraim 95, Jensen 95]
•Signal-dependent transformation
•Signal + noise subspace
2. Multi-microphone optimal filtering
spatial information
robustness
3. Blind transfer function
estimation and
dereverberation
1. Robust broadband
beamforming
Multi-microphone techniques
– fixed beamforming [Dolph 46, Cox 86, Ward 95, Elko 00]
•Fixed directivity pattern
– adaptive beamforming [Frost 72, Griffiths 82, Gannot 01]
•adapt to different acoustic environments performance
•`Generalised Sidelobe Canceller’ (GSC)
– inverse, matched filtering [Myoshi 88, Flanagan 93, Affes 97]
only spectral information
a-priori assumptions
1111
OverviewOverview
• Introduction
• Basic principles
� Signal model
� Signal characteristics and acoustic environment
• Robust broadband beamforming
• Multi-microphone optimal filtering
• Acoustic transfer function estimation and dereverberation
• Conclusion and further research
1212
Signal modelSignal model
• Signal model for microphone signals in time-domain: filtered version of clean speech signal + additive coloured noise
][0 ky
][1 ky
][1 kyN
][][][ kvkxky nnn ][kvn][khn ][ks
Acousticimpulse response
][ks
Speechsignal
Additivenoise
Introduction
Basic principles -Signal model -Characteristics
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
1313
Signal modelSignal model
• Multi-microphone signal enhancement: microphone signals are filtered with filters wn[k] and summed
� f [k] = total transfer function for speech component
� zv[k] = residual noise component
][
][][][
][
][][][][][1
0
1
0
1
0
kz
kvkwks
kf
khkwkykwkz
v
N
nnn
N
nnn
N
nnn
• Techniques differ in calculation of filters:
� Noise reduction : minimise residual noise zv[k] and limit speech distortion
� Dereverberation : f [k]=δ [k] by estimating acoustic impulse responses hn[k]
� Combined noise reduction and dereverberation
Introduction
Basic principles -Signal model -Characteristics
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
1414
Signal characteristicsSignal characteristics
• Speech:
� Broadband (300-8000 Hz)
� Non-stationary
� On/off-characteristic
Speech detection algorithm (VAD)
� Linear low-rank model: linearcombination of basis functions
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Am
plit
ud
e
Time (sec)
][][1
kak i
R
ii
ss (R=12…20)
• Noise:
� unknown signals (no reference available)
� slowly time-varying (fan) non-stationary (radio, speech)
� localised diffuse noise
Introduction
Basic principles -Signal model -Characteristics
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
1515
Acoustic environmentAcoustic environment
• Reverberation time T60 : global characterisation
• Acoustic impulse responses:
� Acoustic filtering between2 points in a room
� FIR filter (K=1000…2000 taps)
� Non-minimum-phase system no stable inverse
• Microphone array:
� Assumption: point sensors with ideal characteristics
� Deviations: gain, phase, position
� Distance speaker – microphone array: far-field near-field
Car Room Church
70 ms 250 ms 1500 ms
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Time (sec)
Am
plit
ud
e
Impulse response PSK row 9
Introduction
Basic principles -Signal model -Characteristics
Beamforming
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
1616
OverviewOverview
• Introduction
• Basic principles
• Robust broadband beamforming
� Novel design procedures for broadband beamformers
� Robust beamforming for gain and phase errors
• Multi-microphone optimal filtering
• Acoustic transfer function estimation and dereverberation
• Conclusion and further research
1717
Fixed beamformingFixed beamforming
• Speech and noise sources with overlapping spectrum at different positions
Exploit spatial diversity by using multiple microphones
• Technique originally developed for radar applications:
� Smallband : delay compensation broadband
� Far-field : planar waves near-field : spherical waves
� Known sensor characteristics deviations
- Low complexity- Robustness at low signal-to-noise ratio (SNR)
- A-priori knowledge of microphone array characteristics- Signal-independent
FIR filter-and-sum structure: arbitrary spatial directivity pattern for arbitrary microphone array configuration
Suppress noise and reverberation from certain directions
Introduction
Basic principles Beamforming -Design -Robustness
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
1818
Filter-and-sum configurationFilter-and-sum configuration
• Objective: calculate filters wn[k] such that beamformer
performs desired (fixed) spatial and spectral filtering
Far-field: - planar waves- equal attenuation
2D filter design in angle and frequency
Spatial directivity pattern:
),()(
),(),(
gwT
S
ZH
Desired spatial directivity pattern:
),( D
Introduction
Basic principles Beamforming -Design -Robustness
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
1919
Design proceduresDesign procedures
• Design filter w such that spatial directivity pattern optimally fits minimisation of cost function
� Broadband problem: no design for separate frequencies i
design over complete frequency-angle region
� No approximations of integrals by finite Riemann-sum
� Microphone configuration not included in optimisation
• Cost functions:
� Least-squares quadratic function
� Non-linear cost function iterative optimisation = complex!
[Kajala 99]
ddDHFJ LS
2),(),(),()(w
amplitude and phase
ddDHFJ NL
222),(),(),()(w
Double integrals only need to be calculated once
),( H),( D
Introduction
Basic principles Beamforming -Design -Robustness
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
2020
Design proceduresDesign procedures
• 2 non-iterative cost functions, based on eigenfilters:
� Eigenfilters: 1D and 2D FIR filter design
� Extension to design of broadband beamformers
• Novel cost functions:
� Conventional eigenfilter technique (G)EVD
� Eigenfilter based on TLS-criterion GEVD
• Conclusion: TLS-eigenfilter preferred non-iterative design procedure
ddDH
FJtote
TTLS 1
),(),(),()(
2
wQww
[Vaidyanathan 87, Pei 01]
ddHH
D
DFJ cc
cceig
2
),(),(),(
),(),()(w
reference point required
Introduction
Basic principles Beamforming -Design -Robustness
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
2121
Non-linear procedure TLS-Eigenfilter
SimulationsSimulations
Angle (deg) Freq (Hz)
dB
Angle (deg) Freq (Hz)
dB
Parameters:-N=5, d=4cm-L=20, fs=8kHz-Pass: 40o-80o
-Stop: 0o-30o + 90o-180o
Delay-and-sum
Angle (deg) Freq (Hz)
dB
2222
Near-field configurationNear-field configuration
• Near-field: spherical waves + attenuation
• Ultimate goal: design for all distances
• One specific distance: very similar to far-field design (different calculation of double integrals)
• Several distances: trivial extension for most cost functions, for TLS-eigenfilter = sum of generalised Rayleigh-quotients
Take into account distance r between speaker - microphones
Rtot drddrDrHrFJ 2),,(),,(),,()(w
Finite number (R) of distances
R
rrrtot JJ
1
)()( ww
Deviation for other distances
Trade-off performance for different distances
Introduction
Basic principles Beamforming -Design -Robustness
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
2323
Far-field pattern Near-field pattern (r=0.2m)
SimulationsSimulations
Angle (deg)
Frequency (Hz)
dB
Far-fi
eld
desig
n
Angle (deg)
Frequency (Hz)
dB
Mix
ed n
ear-fi
eld
far-
field
Angle (deg)
Frequency (Hz)
dB
Angle (deg)
Frequency (Hz)
dB
Parameters:-N=5, d=4cm-L=20, fs=8kHz-Pass: 70o-110o
-Stop: 0o-60o + 120o-180o
2424
• Small deviations from the assumed microphone characteristics (gain, phase, position) large deviations from desired directivity pattern, especially for small-size microphone arrays
• In practice microphone characteristics are never exactly known
• Consider all feasible microphone characteristics and optimise
� average performance using probability as weight
– requires knowledge about probability density functions
� worst-case performance minimax optimisation problem
– finite grid of microphone characteristics high complexity
Robust broadband beamformingRobust broadband beamforming
101010 )()(),,(0 1
NNN
A A
mean dAdAAfAfAAJJN
Incorporate specific (random) deviations in design
position
/cos
phase
),(
gain
),(),( cfjjnn
snn eeaA
Measurement or calibration procedure
Introduction
Basic principles Beamforming -Design -Robustness
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
2525
SimulationsSimulations
• Non-linear design procedure
• N=3, positions: [-0.01 0 0.015] m, L=20, fs=8 kHz
• Passband = 0o-60o, 300-4000 Hz (endfire)Stopband = 80o-180o, 300-4000 Hz
• Robust design - average performance:Uniform pdf = gain (0.85-1.15) and phase (-5o-10o)
• Deviation = [0.9 1.1 1.05] and [5o -2o 5o]
Design J Jdev Jmean Jmax
Non-robust 0.1585 87.131 275.40 3623.6
Average cost 0.2196 0.2219 0.3371 0.4990
Maximumcost
0.1707 0.1990 0.4114 0.4167
Introduction
Basic principles Beamforming -Design -Robustness
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
2626
Non-robust design Robust design
No d
evia
tions
Devia
tions (g
ain
/phase
)
SimulationsSimulations
Angle (deg)
Frequency (Hz)
dB
Angle (deg)
Frequency (Hz)
dB
Angle (deg)
Frequency (Hz)
dB
Angle (deg)
Frequency (Hz)
dB
Introduction
Basic principles Beamforming -Design -Robustness
Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
Non-robust design Robust design
SimulationsSimulations
2727
2828
OverviewOverview
• Introduction
• Basic principles
• Robust broadband beamforming
• Multi-microphone optimal filtering
� GSVD-based optimal filtering technique
� Reduction of computational complexity
� Simulations
• Acoustic transfer function estimation and dereverberation
• Conclusion and further research
2929
Multi-microphone optimal Multi-microphone optimal filteringfiltering
Objective: optimal estimate of speech components
in microphone signals
Minimise MSE 2][][ kzkxE n No a-priori assumptions
2
][
2
][][][][min][][min kkkEkkE T
kkyWxzx
WW
][][][ 1 kkk yxyyWF RRW
Multi-channel Wiener Filter
][][][][ 1 kkkk vvyyyyWF RRRW
-Speech and noise independent-2nd order statistics noise stationary estimate during noise periods (VAD)
Multi-microphone
Signal-dependent Robustness
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3030
Multi-microphone optimal Multi-microphone optimal filteringfiltering
• Implementation procedure:
� based on Generalised Eigenvalue Decomposition (GEVD)
– take into account low-rank model of speech
– trade-off between noise reduction and speech distortion
� QRD [Rombouts 2002] , subband [Spriet 2001] lower complexity
• Generalised Eigenvalue Decomposition (GEVD):
• Speech detection mechanism is the only a-priori assumption:required for estimation of correlation matrices
][][][][
][][][][
kkkk
kkkkT
vvv
Tyyy
QΛQR
QΛQR
coloured noise!
Low-rank model
MRikk
Rikk
ii
ii
1,][][
1,][][22
22
][][
][1diag][][
2
2
kk
kη-kk T
i
iTWF QQW
Signal-dependent FIR-filterbank
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3131
General class of estimatorsGeneral class of estimators
• Multi-channel Wiener filter: always combination of noise reduction and (linear) speech distortion:
estimation error:
][ke ][][ kkTWFM xWI ][][ kkT
WF vW
• General class: noise reduction speech distortion
– =1 : MMSE (equal importance)
– <1 : less speech distortion, less noise reduction
– >1 : more speech distortion, more noise reduction
[Ephraim 95]
][][)1(][
][][diag][][
22
22
kkηk
kηkkk T
ii
iiTWF QQW
speech distortion
residual noise
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3232
• Decomposition in spectral and spatial filtering term
• Desired beamforming behaviour for simple scenarios
Frequency-domain analysisFrequency-domain analysis
WFW
vx
x
PP
P
1
11 eΓΓ xy
spectral filtering(PSD)
spatial filtering(coherence)
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
Speech Noise
3333
Complexity reductionComplexity reduction
• Recursive version: each time step calculation GSVD + filter
• Complexity reduction using:
� Recursive techniques for recomputing GSVD [Moonen 90]
� Sub-sampling (stationary acoustic environments)
High computational complexity
Batch Recursive QRD [Rombouts]
sub = 1 7504 Gflops 2.1 Gflops 358 Mflops
sub = 20
375 Gflops 105 Mflops 18 Mflops
(N = 4, L = 20, M=80, fs = 16 kHz, P = 4000, Q = 20000)
)(316 23 QPMM 25.20 M 25.3 M
Real-time implementation possible
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3434
Complexity reductionComplexity reduction
• Incorporation in ‘Generalised Sidelobe Canceller’ (GSC) structure: adaptive beamforming
� Creation of speech reference and noise reference signals
� Standard multi-channel adaptive filter (LMS, APA)][0 ky
][1 ky
][1 kyN
Speechreferenc
e
][0 kw
][1 kw
][1 kwN
Optimalfilter
Noise reference(
s) +
–
][0 kwa
Adaptive filter
delay
Increase noise reduction performance
Complexity reduction by using shorter filters
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3535
SimulationsSimulations
• N=4, SNR=0 dB, 3 noise sources (white, speech, music), fs=16 kHz
• Performance: improvement of signal-to-noise ratio (SNR)
0 500 1000 15000
5
10
15
Reverberation time (msec)
Unb
iase
d S
NR
(dB
)Delay-and-sum beamformerGSC (LANC=400, noise ref=Griffiths-Jim)
Recursive GSVD (L=20, LANC=400, all nref)Recursive GSVD (L=20, no ANC)
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3636
SimulationsSimulations
• N=4, SNR=0 dB, 3 noise sources, fs=16 kHz, T60=300 msec
• ‘Power Transfer Functions’ (PTF) for speech and noise component
0 1000 2000 3000 4000 5000 6000 7000 8000
-30
-25
-20
-15
-10
-5
0
Speech
Noise
Frequency (Hz)
Sp
ect
rum
(d
B)
Recursive GSVD (L=20, no ANC)Recursive GSVD (L=20, LANC=400, all noise ref)
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3737
ConclusionsConclusions
• GSVD-based optimal filtering technique:
� Multi-microphone extension of single-microphone subspace-based enhancement techniques
� Signal-dependent low-rank model of speech
� No a-priori assumptions about position of speaker and microphones
• SNR-improvement higher than GSC for all reverberation times and all considered acoustic scenarios
• More robust to deviations from signal model:
� Microphone characteristics
� Position of speaker
� VAD: only a-priori information!
– No effect on SNR-improvement
– Limited effect on speech distortion
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3838
Advantages - DisadvantagesAdvantages - Disadvantages
Fixed beamforming
Adaptivebeamforming
Optimal filtering
Signal-dependent no yes yes
Noise reduction + ++ +++
Dereverberation + + no
Complexity low average high
VAD no yes yes
Robustness - (+) -- (+) ++
Introduction
Basic principles Beamforming Multi-microphoneoptimal filtering
-Optimal filtering -Complexity -Simulations
Transfer functionestimation and dereverberation
Conclusion
3939
OverviewOverview
• Introduction
• Basic principles
• Robust broadband beamforming
• Multi-microphone optimal filtering
• Acoustic transfer function estimation and dereverberation
� Time-domain technique
� Frequency-domain technique
� Combined noise reduction and dereverberation
• Conclusion and further research
4040
ObjectiveObjective
][0 ky
][1 ky
][1 kyN
][1 kh
][0 kw
][1 kw
][1 kwN
][kz
Blind estimation of acoustic impulse responses
Time-domain Frequency-domain
Noise reduction and
dereverberation
Dereverberation
Source localisation
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
-Time-domain -Frequency-domain
-Dereverberation Conclusion
4141
• Signal model for N=2 and no background noise
• Subspace-based technique: impulse responses can be computed from null-space of speech correlation matrix� Eigenvector corresponding to smallest eigenvalue� Coloured noise: GEVD� Problems occuring in time-domain technique:
– sensitivity to underestimation of impulse response length – low-rank model in combination with background noise
Time-domain techniquesTime-domain techniques
S(z)
H0(z)
H1(z) Y1(z)
Y0(z)
Signals
][kyyR
-H1(z)
H0(z)
Null-space
0
±α
±α
E(z)
E(z)
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
-Time-domain -Frequency-domain
-Dereverberation Conclusion
4242
• Batch estimation techniques form basis for deriving adaptive stochastic gradient algorithm
• Usage :� Estimation of partial impulse responses time-delay
estimation for acoustic source localisation� For source localisation adaptive GEVD algorithm is
more robust than adaptive EVD algorithm (and prewhitening) in reverberant environments with a large amount of noise
Stochastic gradient algorithmStochastic gradient algorithm
1][ subject to,][min uRuuRuu
kk vvT
yyT
]1[][]1[
]1[]1[
][][][][][][]1[
][][][
kkk
kk
kkkekkekk
kkke
vvT
vv
T
uRu
uu
uRyuu
yu
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
-Time-domain -Frequency-domain
-Dereverberation Conclusion
4343
• Problems of time-domain technique frequency-domain
• Signal model: rank-1 model
• Estimation of acoustic transfer function vector H() from GEVD of correlation matrices and
� Corresponding to largest generalised eigenvalue no stochastic gradient algorithm available (yet)
� Unknown scaling factor in each frequency bin:
can be determined only if norm is known
algorithm only useful when position of source is fixed (e.g. desktop, car)
Frequency-domain techniquesFrequency-domain techniques
)(
1
1
0
)(
1
1
0
1
1
0
)(
)(
)(
)(
)(
)(
)(
)(
)(
)(
)(
VH
Y
NNN V
V
V
S
H
H
H
Y
Y
Y
)(yyR )(vvR
)(H
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
-Time-domain -Frequency-domain
-Dereverberation Conclusion
4444
Combined noise reduction and Combined noise reduction and dereverberationdereverberation
• Filtering operation in frequency domain:
• Dereverberation: normalised matched filter
• Combined noise reduction and dereverberation:Z() is optimal (MMSE) estimate of S()
� Optimal estimate of s[k] integration of multi-channel Wiener-filter with normalised matched filter
� Trade-off between both objectives
• Implementation: overlap-save
)()()()()()()()()(
VWHWYW H
F
HH SZ
1)( F
2)(
)()(
H
HW d Residual noise
)(ˆ)()(ˆ SHX
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
-Time-domain -Frequency-domain
-Dereverberation Conclusion
4545
SimulationsSimulations
• N=4, d=2 cm, fs=16 kHz, SNR=0 dB, T60= 400 msec
• FFT-size L=1024, overlap R=16
• Performance criteria:
� Signal-to-noise ratio (SNR)
� Dereverberation-index (DI) :
SNR (dB) DI (dB)
Original microphone signal 2.88 4.74
Noise reduction 16.82 4.73
Dereverberation 2.30 0.86
Combined noise reduction and dereverberation
10.12 1.35
dH )()(log20
2
110 HW
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
-Time-domain -Frequency-domain
-Dereverberation Conclusion
4646
SimulationsSimulations
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
-Time-domain -Frequency-domain
-Dereverberation Conclusion
4747
ConclusionConclusion
• Low signal quality due to background noise and reverberation signal enhancement to improve speech intelligibility and ASR performance
Single-microphone techniques: spectral informationStandard beamforming: a-priori assumptions
No a-priori assumptions
Multi-microphone
Signal-dependent
Blind transfer function
estimation and dereverberation
Robust broadband
beamforming
Multi-microphone optimal filtering
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
4848
ContributionsContributions
• Robust broadband beamforming:� novel cost functions for broadband far-field design
(non-linear, eigenfilter-based)
� extension to near-field and mixed near-field far-field
� 2 procedures for robust design against gain and phase deviations
• GSVD-based optimal filter technique for multi-microphone noise reduction:� extension of single-microphone subspace-based
techniques multiple microphones
� integration in GSC-structure
� better performance and robustness than beamforming
• Acoustic transfer function estimation and dereverberation:� stochastic gradient algorithm for estimation of time-delay
and acoustic source localisation (coloured noise)
� combined noise reduction and dereverberation in frequency-domain
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
4949
Further researchFurther research
• Combination of multi-channel Wiener-filter and fixed beamforming:
� Low SNR: VAD fails poor performance of Wiener-filter
� Combined technique: more robust when VAD fails, better performance than fixed beamformers in other scenarios
• Acoustic transfer function estimation and dereverberation:
� Time-domain: underlying reason for high sensitivity
� Frequency-domain: unknown scaling factor BSS ?
� other blind identification techniques (LP, NL Kalman-filtering)
• Further complexity reduction of multi-channel optimal filtering technique
� Stochastic gradient algorithms
� Subband/frequency-domain
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion
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Relevant publicationsRelevant publications
• S. Doclo and M. Moonen, “GSVD-based optimal filtering for single and multimicrophone speech enhancement,” IEEE Trans. Signal Processing, vol. 50, no. 9, pp. 2230-2244, Sep. 2002.
• S. Doclo and M. Moonen, “Multi-Microphone Noise Reduction Using Recursive GSVD-Based Optimal Filtering with ANC Postprocessing Stage,” Accepted for publication in IEEE Trans. Speech and Audio Processing, 2003.
• S. Doclo and M. Moonen, “Robust adaptive time delay estimation for speaker localisation in noisy and reverberant acoustic environments, EURASIP Journal on Applied Signal Processing, Sep. 2003.
• S. Doclo and M. Moonen, “Combined frequency-domain dereverberation and noise reduction technique for multi-microphone speech enhancement,” in Proc. Int. Workshop on Acoustic Echo and Noise Control (IWAENC), Darmstadt, Germany, Sep. 2001, pp. 31-34.
• S. Doclo and M. Moonen, “Design of far-field and near-field broadband beamformers using eigenfilters,” Accepted for publication in Signal Processing, 2003.
• S. Doclo and M. Moonen, “Design of robust broadband beamformers for gain and phase errors in the microphone array characteristics,” IEEE Trans. Signal Processing, Oct. 2003.
Available at http://www.esat.kuleuven.ac.be/~doclo/publications.html
Introduction Basic principles Beamforming Multi-microphoneoptimal filtering
Transfer functionestimation and dereverberation
Conclusion