DIT - University of Trento Techniques for robust source separation and localization in adverse ...

216 Pages·2010·7.66 MB·English

PhDDissertation
InternationalDoctorateSchoolinInformationand
CommunicationTechnologies
DIT - University of Trento
Techniques for robust source separation and localization in
adverse environments
Issuesandperformanceofanewframeworkofemergingtechniquesforfrequency-domainconvolutive
blind/semi-blindseparationandlocalizationofacousticsources
Francesco Nesta
Advisor:
Prof. MaurizioOmologo
FBK-irstFondazioneBrunoKessler
March2010
”Doubt is the only certainty”
...someoneIdon’tremember
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Acknowledgment
Iwouldliketoexpressmygratitudetoallthepeoplewhowalkedwithmeduringthisadventure.
In particular, to my advisor Maurizio Omologo for his moral support and totally confidence
in my work even when I felt to be ”in a dead-end street”. To the FBK and the University
of Trento which gave me an outstanding support to present my work and funny demos to a
wide international audience. To all my colleagues for their helpfulness in solving my technical
problems and willingness to listen my boring and strange theories. To prof. Fred Juang and
all the colleagues of Georgia Tech which made me grow professionally with them. To all my
room-matesforactinginterestinmyenthusiasmeverytimeIthoughttohavediscoveredanew
wheel. To all the new international friends, which made these years of my life an exciting time
andtoallmyoldfriendsforneverlettingmeforgetmyorigin. Andlast,butnotforimportance,
I want to thank my parents and my brothers for always encouraging me when something was
wrongandGeiza,forkeepingmesaneduringthelastfreneticmonths. Finally,aspecialthanks
tothespiritsthatguidedmeonthischallengingbutalwaysworthfulexperience.

Abstract
Acousticsourceseparationisarelativelyrecenttopicofsignalprocessingwhichaimstosimul-
taneously separate many acoustic sources recorded through one or more microphones. Such a
problem was formulated to emulate the natural capability of the human auditory system which
is able to recognize and enhance the sound coming from a particular source. Addressing this
problemisofhighinterestintheautomaticspeechrecognition(ASR)communitysinceitwould
improve the effectiveness of a natural human-machine interaction. Among numerous methods
of multichannel blind source separation techniques, those based on the Independent Compo-
nent Analysis (ICA) applied in the frequency-domain [81] are the most investigated, due to
their straightforward physical interpretation and computational efficiency. In spite of recent
developments many issues still need to be address to make such techniques robust in adverse
conditions, such as high reverberation, ill-conditioning and occurrence of permutations. Fur-
thermore,mostoftheproposedBSSmethodsarecomputationallyexpensiveandnotfeasiblefor
areal-timeimplementation.
This PhD thesis describes a research activity in the robust separation of acoustic sources in
adverse environment. A new framework of blind and semi-blind techniques is proposed which
allowssourcelocalizationandseparationeveninhighlyreverberantenvironmentandwithreal-
timeconstraint. Foreachproposedtechnique,theoreticalandpracticalissuesarediscussedand
a comparison with alternative state-of-art methods is provided. Furthermore, the robustnessof
the proposed framework is validated implementing two real-time blind and semi-blind systems
whicharetestedinchallengingreal-worldscenarios.
Keywords [blind source separation, source localization, acoustic echo cancellation, indepen-
dentcomponentanalysis,permutationproblem]
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Contents
1 Introduction 1
1.1 TheProblemofSourceEnhancement . . . . . . . . . . . . . . . . . . . . . . 1
1.2 MultichannelSourceEnhancement . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 FixedBeamformers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.3 AdaptiveBeamformers . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 ScopeandThesisOutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4.1 InternationalJournalPublications . . . . . . . . . . . . . . . . . . . . 10
1.4.2 InternationalConferencePublications . . . . . . . . . . . . . . . . . . 10
2 AnintroductiontoBSS 13
2.1 InstantaneousLinearModel . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1.1 PrincipalComponentAnalysis . . . . . . . . . . . . . . . . . . . . . . 16
2.1.2 IndependentComponentAnalysis . . . . . . . . . . . . . . . . . . . . 17
2.1.3 ICABasedonNongaussianity . . . . . . . . . . . . . . . . . . . . . . 20
2.2 NGandKLdivergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 BSSforconvolutivemixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3.1 MainframeworkforconvolutiveBSS . . . . . . . . . . . . . . . . . . 24
3 Frequency-DomainBSS 31
3.0.2 OutputSourceEstimation . . . . . . . . . . . . . . . . . . . . . . . . 32
3.0.3 ScalingAmbiguityandMinimalDistortionPrinciple(MDP) . . . . . . 33
3.0.4 CircularityofFFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.1 PermutationProblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 PhysicalInterpretationofFD-BSS . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 ICAforFD-BSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
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4 RecursiveConvolutiveICA 43
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 RR-ICA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2.1 Wiener-likeweighting . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2.2 Relationship between Wiener-like weighted ICA and Adaptive Beam-
Formers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2.3 LeastMeanSquaretrackingofthedemixingmatrix . . . . . . . . . . . 48
4.2.4 Proposedalgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2.5 RR-ICAandOthersBSS . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3 Experimentalresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.1 Experimentswithsimulateddata . . . . . . . . . . . . . . . . . . . . . 52
4.3.2 Experimentswithreal-worlddata . . . . . . . . . . . . . . . . . . . . 56
4.4 ConcludingRemarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.5 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5 GSCT 65
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2 FD-BSSandidealacousticpropagation . . . . . . . . . . . . . . . . . . . . . 66
5.3 StateCoherenceTransform . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.3.1 EquivalenceBetweenSCTandMLE . . . . . . . . . . . . . . . . . . 70
5.3.2 EquivalenceBetweenSCTandKernelpdfEstimation . . . . . . . . . 72
5.3.3 Definitionof g(·)andAdaptiveEstimationof α . . . . . . . . . . . . . 74
5.3.4 ConnectionsBetweenSCTandGCC-PHAT . . . . . . . . . . . . . . . 76
5.3.5 NumericalEvaluationoftheSCT . . . . . . . . . . . . . . . . . . . . 78
5.4 GeneralizedStateCoherenceTransform . . . . . . . . . . . . . . . . . . . . . 79
5.4.1 NumericalEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.4.2 GSCTandComputationalIssues . . . . . . . . . . . . . . . . . . . . . 83
5.5 SolvingReal-WorldproblemswiththeSCT/GSCT . . . . . . . . . . . . . . . 85
5.5.1 SpatialLocalizationofMultipleSources; . . . . . . . . . . . . . . . . 85
5.5.2 ReductionofPermutations . . . . . . . . . . . . . . . . . . . . . . . . 92
5.5.3 UnderdeterminedSourceSeparationbySpatialFiltering . . . . . . . . 101
5.6 ConcludingRemarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6 cSPEC 105
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.2 LimitationofSCTandPermutationProblem . . . . . . . . . . . . . . . . . . . 106
6.3 Correlation-basedApproachesandTheirLimitations . . . . . . . . . . . . . . 107
6.4 LocallyandGloballyCoherentSpectrumEstimation . . . . . . . . . . . . . . 110
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6.5 ProposedAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.6 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
6.7 ConcludingRemarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
7 SBSSappliedtoMCAEC 115
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
7.2 AreviewoftheLMSalgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 117
7.3 TheoreticalanalysisofSBSS . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
7.3.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
7.3.2 Non-UniquenessProblem . . . . . . . . . . . . . . . . . . . . . . . . 124
(cid:98)
7.3.3 Steady-StateSolutionfor H . . . . . . . . . . . . . . . . . . . . . . 125
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7.3.4 ConnectionbetweenMSEandICA . . . . . . . . . . . . . . . . . . . 128
7.3.5 EffectofConstrainton W(ω) . . . . . . . . . . . . . . . . . . . . . . 129
7.4 Algorithmicdesignandrelatedissues . . . . . . . . . . . . . . . . . . . . . . 132
7.4.1 OnlineImplementationofSBSS . . . . . . . . . . . . . . . . . . . . . 132
7.4.2 ProposedSBSSAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . 135
7.5 Experimentalevaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
7.5.1 EvaluationMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
7.5.2 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
7.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
7.7 ConcludingRemarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
8 TowardsDistributedBSS 153
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
8.2 BSSasSoft-MasksEstimation . . . . . . . . . . . . . . . . . . . . . . . . . . 154
8.3 CooperativeWiener-ICA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
8.4 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8.5 ConcludingRemarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
9 Real-timeBSS/SBSS 161
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
9.2 ArchitectureoftheBSSsystem . . . . . . . . . . . . . . . . . . . . . . . . . . 161
9.2.1 Inputprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
9.2.2 ICA-BSS(first-stage) . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
9.2.3 SCT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
9.2.4 ICA-BSS(secondstage) . . . . . . . . . . . . . . . . . . . . . . . . . 164
9.2.5 Outputprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
9.3 EvaluationoftheBSSalgorithm . . . . . . . . . . . . . . . . . . . . . . . . . 166
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9.4 Architectureofthereal-timeSBSS . . . . . . . . . . . . . . . . . . . . . . . . 173
9.4.1 Inputprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
9.4.2 ICA-SBSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
9.4.3 Outputprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
9.4.4 BSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
9.4.5 Start-endpointdetector . . . . . . . . . . . . . . . . . . . . . . . . . . 181
9.4.6 ASR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
9.5 EvaluationoftheSBSSalgorithm . . . . . . . . . . . . . . . . . . . . . . . . 182
10 Conclusions 187
Bibliography 187
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