PDF-IEEETRANSACTIONSONAUDIO,SPEECH,ANDLANGUAGEPROCESSING,VOL.18,NO.6,AUGUS

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1602IEEETRANSACTIONSONAUDIOSPEECHANDLANGUAGEPROCESSINGVOL18NO6AUGUST2010 Fig1AnexampleofmeetingdataDialogacttagsandaddressedpersonsareshowninparanthesesThismeetingdatahasoneactionitemandone

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IEEETRANSACTIONSONAUDIO,SPEECH,ANDLANGUAGEPROCESSING,VOL.18,NO.6,AUGUS: Transcript

1602IEEETRANSACTIONSONAUDIOSPEECHANDLANGUAGEPROCESSINGVOL18NO6AUGUST2010 Fig1AnexampleofmeetingdataDialogacttagsandaddressedpersonsareshowninparanthesesThismeetingdatahasoneactionitemandone. Perhap h ma no g t Rom t mak it a som politician s preten tha th Cardinal retrea t Bologn wa o purpos t o giv hi a opportunit t mak a visi t hi fathe withou break in hi oat o neve meetin hi brothe i tha habit Stosc i s stil privatel o a opinio tha h Fig.6.Caseofunknownnumberofsources.Itwasassumedthatwherethenumberofsourceswasactuallyequalto2.Left:separatedsignalsusingIVA.Right:separatedsignalsusingG-IVA.B.UnknownNumberofSourcesUsingaCompleteSetti .Sincethemodi Melody Extraction(Dec.2011).leadingprinciples[19],noveltechniquesaredevelopedforad-dressingthechallengesmentionedearlier–voicingdetection,avoidingoctaveerrorsandselectingthepitchcontoursthatbelon WUANDWANG:TWO-STAGEALGORITHMFORONE-MICROPHONEREVERBERANTSPEECHENHANCEMENT775Reverberantspeechenhancementusingonemicrophoneiscantlymorechallengingthanthatusingmultiplemicro-phones.Nonetheless,anumberof , , , forthematrix/vectortranspose,theinverse,theMoore Meaning TnumberofframesinaspeechsequenceXsequenceofcleanspeechvectors(x1;x2;::::::;xT)Ysequenceofdistortedspeechvectors(y1;y2;::::::;yT)sequenceofspeechstates(1;2;::::::;T)acousticmodelparameter JacekP.DmochowskiwasborninGdansk,Poland,inDecember1979.HereceivedtheB.Eng.degree(withhighdistinction)incommunicationsengi-neeringandtheM.S.degreeinelectricalengineeringfromCarletonUniversity,Ottawa,ON (2)where isthepriorprobabilityofeachmixture,and isamulti-dimensional(19-D)Gaussianwithanobservationvector ,meanvector ,andcovariancematrix .Here,onlyadiagonalcovariancematrixisconsidered.Next,allmixtu Fig.1.Blockdiagramofechocancellationsystemondesigningthelearningratetoreactquicklyindouble-talkconditions.InSectionII,wederivetheoptimallearningratefortheNLMSalgorithminpresenceofnoise.InSectionIII,we Fig.3.Adaptivecontrolofthescalingfactortoreachwatermarkinaudibility. Fig.4.Modulatorscheme,usingalocalcopyofthereceiver.A.UsingtheLocalCopyoftheReceiverThelocalcopyofthereceiverallowsustoestimatethesi Fig.1.Blockdiagramofechocancellationsystemondesigningthelearningratetoreactquicklyindouble-talkconditions.InSectionII,wederivetheoptimallearningratefortheNLMSalgorithminpresenceofnoise.InSectionIII,we Fig.1.Schematicdiagramoftheproposedsystem.Areverberantmixtureisprocessedinathree-stagesystem.Therststageanalyzestheinputsignalbyanauditorylterbankinsuccessivetimeframesandextractspitch-basedfeatures (Augus BUREA SUNDRY NOTICES AND ON WELLS^^pj^; not use this form for proposals to drill or t&fe^nter^ah, well. Use Form 3160-3 . Form 3160-5 STATES I (Augus BUREA SUNDRY NOTICES AND ON WELLS^^pj^; not

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