Microphone Array Speech Processing

94 Pages·7.136 MB·English

by  Nordholm S. etc. (eds.)

EURASIP Journal on Advances in Signal Processing
Microphone Array Speech Processing
Guest Editors: Sven Nordholm, Thushara Abhayapala, Simon Doclo,
Sharon Gannot, Patrick Naylor, and Ivan Tashev
Microphone Array Speech Processing
EURASIP Journal on
Advances in Signal Processing
Microphone Array Speech Processing
Guest Editors: Sven Nordholm, Thushara Abhayapala,
Simon Doclo, Sharon Gannot, Patrick Naylor,
and Ivan Tashev
Copyright©2010HindawiPublishingCorporation.Allrightsreserved.
Thisisaspecialissuepublishedinvolume2010of“EURASIPJournalonAdvancesinSignalProcessing.”Allarticlesareopenaccess
articlesdistributedundertheCreativeCommonsAttributionLicense,whichpermitsunrestricteduse,distribution,andreproductionin
anymedium,providedtheoriginalworkisproperlycited.
Editor-in-Chief
PhillipRegalia,InstitutNationaldesTe´le´communications,France
Associate Editors
AdelM.Alimi,Tunisia SudharmanK.Jayaweera,USA DouglasO’Shaughnessy,Canada
KennethBarner,USA SorenHoldtJensen,Denmark Bjo¨rnOttersten,Sweden
YasarBecerikli,Turkey MarkKahrs,USA JacquesPalicot,France
KostasBerberidis,Greece MoonGiKang,SouthKorea AnaPerez-Neira,Spain
EnricoCapobianco,Italy WalterKellermann,Germany WilfriedR.Philips,Belgium
A.EnisCetin,Turkey LisimachosP.Kondi,Greece AggelosPikrakis,Greece
JonathonChambers,UK AlexChichungKot,Singapore IoannisPsaromiligkos,Canada
Mei-JuanChen,Taiwan ErcanE.Kuruoglu,Italy AthanasiosRontogiannis,Greece
Liang-GeeChen,Taiwan TanLee,China GregorRozinaj,Slovakia
SatyaDharanipragada,USA GeertLeus,TheNetherlands MarkusRupp,Austria
KutluyilDogancay,Australia T.-H.Li,USA WilliamSandham,UK
FlorentDupont,France HushengLi,USA B.Sankur,Turkey
FrankEhlers,Italy MarkLiao,Taiwan ErchinSerpedin,USA
SharonGannot,Israel Y.-P.Lin,Taiwan LingShao,UK
SamanwoyGhosh-Dastidar,USA ShojiMakino,Japan DirkSlock,France
NorbertGoertz,Austria StephenMarshall,UK Yap-PengTan,Singapore
M.Greco,Italy C.Mecklenbra¨uker,Austria Joa˜oManuelR.S.Tavares,Portugal
IreneY.H.Gu,Sweden GloriaMenegaz,Italy GeorgeS.Tombras,Greece
FredrikGustafsson,Sweden RicardoMerched,Brazil DimitriosTzovaras,Greece
UlrichHeute,Germany MarcMoonen,Belgium BernhardWess,Austria
SangjinHong,USA ChristophorosNikou,Greece Jar-FerrYang,Taiwan
JiriJan,CzechRepublic SvenNordholm,Australia AzzedineZerguine,SaudiArabia
MagnusJansson,Sweden PatrickOonincx,TheNetherlands AbdelhakM.Zoubir,Germany
Contents
MicrophoneArraySpeechProcessing,SvenNordholm,ThusharaAbhayapala,SimonDoclo,
SharonGannot(EURASIPMember),PatrickNaylor,andIvanTashev
Volume2010,ArticleID694216,3pages
SelectiveFrequencyInvariantUniformCircularBroadbandBeamformer,XinZhang,WeeSer,
ZhangZhang,andAnoopKumarKrishna
Volume2010,ArticleID678306,11pages
First-OrderAdaptiveAzimuthalNull-SteeringfortheSuppressionofTwoDirectionalInterferers,
Rene´ M.M.Derkx
Volume2010,ArticleID230864,16pages
Musical-NoiseAnalysisinMethodsofIntegratingMicrophoneArrayandSpectralSubtractionBasedon
Higher-OrderStatistics,YuTakahashi,HiroshiSaruwatari,KiyohiroShikano,andKazunobuKondo
Volume2010,ArticleID431347,25pages
MicrophoneDiversityCombiningforIn-CarApplications,Ju¨rgenFreudenberger,SebastianStenzel,
andBenjaminVenditti
Volume2010,ArticleID509541,13pages
DOAEstimationwithLocal-Peak-WeightedCSP,OsamuIchikawa,TakashiFukuda,
andMasafumiNishimura
Volume2010,ArticleID358729,9pages
ShooterLocalizationinWirelessMicrophoneNetworks,DavidLindgren,OlofWilsson,
FredrikGustafsson,andHansHabberstad
Volume2010,ArticleID690732,11pages
HindawiPublishingCorporation
EURASIPJournalonAdvancesinSignalProcessing
Volume2010,ArticleID694216,3pages
doi:10.1155/2010/694216
Editorial
Microphone Array Speech Processing
SvenNordholm(EURASIPMember),1ThusharaAbhayapala(EURASIPMember),2
SimonDoclo(EURASIPMember),3SharonGannot(EURASIPMember),4
PatrickNaylor(EURASIPMember),5andIvanTashev6
1DepartmentofElectricalandComputerEngineering,CurtinUniversityofTechnology,Perth,WA6845,Australia
2CollegeofEngineering&ComputerScience,TheAustralianNationalUniversity,Canberra,ACT0200,Australia
3InstituteofPhysics,SignalProcessingGroup,UniversityofOldenburg,26111Oldenburg,Germany
4SchoolofEngineering,Bar-IlanUniversity,52900TelAviv,Israel
5DepartmentofElectricalandElectronicEngineering,ImperialCollege,LondonSW72AZ,UK
6MicrosoftResearch,USA
CorrespondenceshouldbeaddressedtoSvenNordholm,[email protected]
Received21July2010;Accepted21July2010
Copyright©2010Sven Nordholm et al. This is an open access article distributed under the Creative Commons Attribution
License,whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperly
cited.
Significant knowledge about microphone arrays has been highlyreverberantspeechgiventhatweonlycanobservethe
gainedfromyearsofintenseresearchandproductdevelop- receivedmicrophonesignals.
ment.Therehavebeennumerousapplicationssuggested,for This special issue contains contributions to traditional
example, from large arrays (in the order of >100 elements) areas of research such as frequency invariant beamforming
for use in auditoriums to small arrays with only 2 or 3 [1], hand-free operation of microphone arrays in cars [2],
elementsforhearingaidsandmobiletelephones.Apartfrom and source localisation [3]. The contributions show new
that,microphone arraytechnology hasbeen widelyapplied ways to study these traditional problems and give new
inspeechrecognition,surveillance,andwarfare.Traditional insights into those problems. Small size arrays have always
techniques that have been used for microphone arrays a lot of applications and interest for mobile terminals,
include fixed spatial filters, such as, frequency invariant hearing aids, and close up microphones [4]. The novel
beamformers, optimal and adaptive beamformers. These way to represent small size arrays leads to a capability to
array techniques assume either model knowledge or cali- suppress multiple interferers. Abnormalities in noise and
brationsignalknowledgeaswellaslocalizationinformation speech stemming from processing are largely unavoidable,
for their design. Thus they usually combine some form and using nonlinear processing results often in significant
of localisation and tracking with the beamforming. Today character change particularly in noise character. It is thus
contemporarytechniquesusingblindsignalseparation(BSS) important to provide new insights into those phenomena
and time frequency masking technique have attracted sig- particularly the so called musical noise [5]. Finally, new
nificantattention.Thosetechniquesarelessreliantonarray and unusual use of microphone arrays is always interesting
modelandlocalization,butmoreonthestatisticalproperties to see. Distributed microphone arrays in a sensor network
of speech signals such as sparseness, non-Gaussianity, and [6] provide a novel approach to find snipers. This type of
non-stationarity. The main advantage that multiple micro- processinghasgoodopportunitiestogrowininterestfornew
phones add from a theoretical perspective is the spatial andimprovedapplications.
diversity, which is an effective tool to combat interference, The contributions found in this special issue can be
reverberation,andnoise.Theunderpinningphysicalfeature categorized to three main aspects of microphone array
used is a difference in coherence in the target field (speech processing:(i)microphonearraydesignbasedoneigenmode
signal)versusthenoisefield.Viewingtheprocessinginthis decomposition[1,4];(ii)multichannelprocessingmethods
way one can understand also the difficulty in enhancing [2,5];and(iii)sourcelocalisation[3,6].
2 EURASIPJournalonAdvancesinSignalProcessing
ThepaperbyZhangetal.,“Selectivefrequencyinvariant array signal processing and spectral subtraction. To obtain
uniform circular broadband beamformer” [1], describes a better noise reduction, methods of integrating microphone
design method for Frequency-Invariant (FI) beamforming. arraysignalprocessingandnonlinearsignalprocessinghave
This problem is a well-known array signal processing tech- beenresearched.However,nonlinearsignalprocessingoften
niqueusedinmanyapplicationssuchas,speechacquisition, generates musical noise. Since such musical noise causes
acoustic imaging and communications purposes. However, discomfort to users, it is desirable that musical noise is
many existing FI beamformers are designed to have a mitigated. Moreover, it has been recently reported that
frequency invariant gain over all angles. This might not be higher-order statistics are strongly related to the amount
necessary and if a gain constraint is confined to a specific of musical noise generated. This implies that it is possible
angle,thentheFIperformanceoverthatselectedregion(in to optimize the integration method from the viewpoint of
frequency and angle) can be expected to improve. Inspired not only noise reduction performance but also the amount
by this idea, the proposed algorithm attempts to optimize of musical noise generated. Thus, the simplest methods
thefrequencyinvariantbeampatternsolelyforthemainlobe of integration, that is, the delay-and-sum beamformer and
andrelaxtheFIrequirementonthesidelobes.Thissacrifice spectralsubtraction,areanalysedandthefeaturesofmusical
on performance in the undesired region is traded off for noisegeneratedbyeachmethodareclarified.Asaresult,itis
better performance in the desired region as well as reduced clarifiedthataspecificstructureofintegrationispreferable
number of microphones employed. The objective function from the viewpoint of the amount of generated musical
is designed to minimize the overall spatial response of the noise. The validity of the analysis is shown via a computer
beamformer with a constraint on the gain being smaller simulationandasubjectiveevaluation.
thanapredefinedthresholdvalueacrossaspecificfrequency ThepaperbyFreudenbergeretal.,“Microphonediversity
rangeandataspecificangle.Thisproblemisformulatedasa combining for in-car applications” [2], proposes a frequency
convex optimization problem and the solution is obtained domain diversity approach for two or more microphone
by using the Second-Order Cone Programming (SOCP) signals, for example, for in-car applications. The micro-
technique. An analysis of the computational complexity phones should be positioned separately to ensure diverse
of the proposed algorithm is presented as well as its signal conditions and incoherent recording of noise. This
performance. The performance is evaluated via computer enables a better compromise for the microphone position
simulation for different number of sensors and different withrespecttodifferentspeakersizesandnoisesources.This
thresholdvalues.Simulationresultsshowthattheproposed work proposes a two-stage approach: In the first stage, the
algorithm is able to achieve a smaller mean square error of microphonesignalsareweightedwithrespecttotheirsignal-
thespatialresponsegainforthespecificFIregioncompared to-noiseratioandthensummedsimilartomaximum-ratio-
toexistingalgorithms. combining.Thecombinedsignalisthenusedasareference
ThepaperbyDerkx,“First-orderazimuthalnull-steering forafrequencydomainleast-mean-squares(LMS)filterfor
for the suppression of two directional interferers” [4] shows eachinputsignal.TheoutputSNRissignificantlyimproved
thatanazimuthsteerablefirst-ordersuperdirectionalmicro- comparedtocoherence-basednoisereductionsystems,even
phoneresponsecanbeconstructedbyalinearcombination ifonemicrophoneisheavilycorruptedbynoise.
of three eigenbeams: a monopole and two orthogonal The paper by Ichikawa et al., “DOA estimation with
dipoles. Although the response of a (rotation symmetric) local-peak-weighted CSP” [3], proposes a novel weighting
first-order response can only exhibit a single null, the algorithm for Cross-power Spectrum Phase (CSP) analysis
paper studies a slice through this beampattern lying in the to improve the accuracy of direction of arrival (DOA)
azimuthal plane. In this way, a maximum of two nulls estimation for beamforming in a noisy environment. As
in the azimuthal plane can be defined. These nulls are a sound source, a human speaker is used, and as a noise
symmetric with respect to the main-lobe axis. By placing source broadband automobile noise is used. The harmonic
these two nulls on maximally two-directional sources to structures in the human speech spectrum can be used for
be rejected and compensating for the drop in level for the weighting the CSP analysis, because harmonic bins must
desireddirection,thesedirectionalsourcescanbeeffectively contain more speech power than the others and thus give
rejectedwithoutattenuatingthedesiredsource.Anadaptive us more reliable information. However, most conventional
null-steering scheme for adjusting the beampattern, which methods leveraging harmonic structures require pitch esti-
enablesautomaticsourcesuppression,ispresented.Closed- mation with voiced-unvoiced classification, which is not
form expressions for this optimal null-steering are derived, sufficiently accurate in noisy environments. The suggested
enabling the computation of the azimuthal angles of the approach employs the observed power spectrum, which is
interferers. It is shown that the proposed technique has a directly converted into weights for the CSP analysis by
gooddirectivityindexwhentheangulardifferencebetween retaining only the local peaks considered to be coming
the desired source and each directional interferer is at least fromaharmonicstructure.Thepresentedresultsshowthat
90degrees. the proposed approach significantly reduces the errors in
In the paper by Takahashi et al. “Musical noise analysis localization, and it also shows further improvement when
in methods of integrating microphone array and spectral usedwithotherweightingalgorithms.
subtractionbased onhigher-order statistics”[5],anobjective The paper by Lindgren et al., “Shooter localization in
analysis on musical noise is conducted. The musical noise wireless microphone networks” [6], is an interesting com-
is generated by two methods of integrating microphone bination of microphone array technology with distributed
EURASIPJournalonAdvancesinSignalProcessing 3
communications. By detecting the muzzle blast as well as
the ballistic shock wave, the microphone array algorithm
is able to locate the shooter in the case when the sensors
are synchronized. However, in the distributed sensor case,
synchronizationiseithernotachievableorveryexpensiveto
achieveandthereforetheaccuracyoflocalizationcomesinto
question.Fieldtrialsaredescribedtosupportthealgorithmic
development.
SvenNordholm
ThusharaAbhayapala
SimonDoclo
SharonGannot
PatrickNaylor
IvanTashev
References
[1] X. Zhang, W. Ser, Z. Zhang, and A. K. Krishna, “Selective
frequencyinvariantuniformcircularbroadbandbeamformer,”
EURASIP Journal on Advances in Signal Processing, vol. 2010,
ArticleID678306,11pages,2010.
[2] J. Freudenberger, S. Stenzel, and B. Venditti, “Microphone
diversitycombiningforIn-carapplications,”EURASIPJournal
onAdvancesinSignalProcessing,vol.2010,ArticleID509541,
13pages,2010.
[3] O.Ichikawa,T.Fukuda,andM.Nishimura,“DOAestimation
withlocal-peak-weightedCSP,”EURASIPJournalonAdvances
inSignalProcessing,vol.2010,ArticleID358729,9pages,2010.
[4] R.M.M.Derkx,“First-orderadaptiveazimuthalnull-steering
for the suppression of two directional interferers,” EURASIP
JournalonAdvancesinSignalProcessing,vol.2010,ArticleID
230864,16pages,2010.
[5] Yu. Takahashi, H. Saruwatari, K. Shikano, and K. Kondo,
“Musical-noiseanalysisinmethodsofintegratingmicrophone
arrayandspectralsubtractionbasedonhigher-orderstatistics,”
EURASIP Journal on Advances in Signal Processing, vol. 2010,
ArticleID431347,25pages,2010.
[6] D. Lindgren, O. Wilsson, F. Gustafsson, and H. Habberstad,
“Shooterlocalizationinwirelesssensornetworks,”inProceed-
ingsofthe12thInternationalConferenceonInformationFusion
(FUSION’09),pp.404–411,July2009.
HindawiPublishingCorporation
EURASIPJournalonAdvancesinSignalProcessing
Volume2010,ArticleID678306,11pages
doi:10.1155/2010/678306
Research Article
Selective Frequency Invariant Uniform
Circular Broadband Beamformer
XinZhang,1WeeSer,1ZhangZhang,1andAnoopKumarKrishna2
1CenterforSignalProcessing,NanyangTechnologicalUniversity,50NanyangAvenue,Singapore639798
2EADSInnovationWorks,EADSSingaporePteLtd.,No.41,ScienceParkRoad,01-30,Singapore117610
CorrespondenceshouldbeaddressedtoXinZhang,zhang [email protected]
Received16April2009;Revised24August2009;Accepted3December2009
AcademicEditor:ThusharaAbhayapala
Copyright©2010XinZhangetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.
Frequency-Invariant(FI)beamformingisawellknownarraysignalprocessingtechniqueusedinmanyapplications.Inthispaper,
analgorithmthatattemptstooptimizethefrequencyinvariantbeampatternsolelyforthemainlobe,andrelaxtheFIrequirement
onthesidelobeisproposed.Thissacrificeonperformanceintheundesiredregionistradedoffforbetterperformanceinthe
desiredregionaswellasreducednumberofmicrophonesemployed.Theobjectivefunctionisdesignedtominimizetheoverall
spatialresponseofthebeamformerwithaconstraintonthegainbeingsmallerthanapre-definedthresholdvalueacrossaspecific
frequencyrangeandataspecificangle.Thisproblemisformulatedasaconvexoptimizationproblemandthesolutionisobtained
byusingtheSecondOrderConeProgramming(SOCP)technique.Ananalysisofthecomputationalcomplexityoftheproposed
algorithmispresentedaswellasitsperformance.Theperformanceisevaluatedviacomputersimulationfordifferentnumber
ofsensorsanddifferentthresholdvalues.Simulationresultsshowthat,theproposedalgorithmisabletoachieveasmallermean
squareerrorofthespatialresponsegainforthespecificFIregioncomparedtoexistingalgorithms.
1.Introduction to use the Frequency-Invariant (FI) beampattern synthesis
technique. As the name implies, such beamformers are
Broadband beamforming techniques using an array of designed to have constant spatial gain response over the
microphoneshavebeenappliedwidelyinhearingaids,tele- desiredfrequencybands.
conferencing, and voice-activated human-computer inter- Over recent years, FI beamforming techniques are
face applications. Several broadband beamformer designs developed in a fast pace. It is difficult to make a distinct
have been reported in the literature [1–3]. One design classification.However,inordertograsptheliteratureonFI
approachistodecomposethebroadbandsignalintoseveral beamforminginaglimpse,weclassifythemlooselyintothe
narrowband signals and apply narrowband beamforming followingthreetypes.
techniques for each narrowband signal [4]. This approach One type of FI beamformers includes those that focus
requires several narrowband processing to be conducted on the design based on array geometry. These include, for
simultaneously and is computationally expensive. Another example, the 3D sensor array design reported in [6], the
designapproachistouseadaptivebroadbandbeamformers. rectangular sensor array design reported in [7], and the
Such techniques use a bank of linear transversal filters to designofusingsubarraysin[8].In[9],theFIbeampatternis
generatethedesiredbeampattern.Thefiltercoefficientscan achievedbyexploitingtherelationshipamongthefrequency
be derived adaptively from the received signals. One classic responsesofthevariousfiltersimplementedattheoutputof
design example is the Frost Beamformer [5]. However, in eachsensor.
ordertohaveasimilarbeampatternovertheentirefrequency The second type of FI beamformers is designed on
range, a large number of sensors and filter taps will be the base of a least-square approach. For this type of FI
needed.Thisagainleadstohighcomputationalcomplexity. beamformers,theweightsofthebeamformerareoptimized
Thethirdapproachofdesigningbroadbandbeamformersis such that the error between the actual beampattern and