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AnalysisonClassicAlgorithmsforSpeechEnhancementSpectralSubtractionWienerFilterKalmanFilter123Conclusion4SpectralSubtraction1

basicspectralsubtraction

improvedspectralsubtraction

FFTontheassumptionthatthespeechareunattachedtothenoisebasicspectralsubtractionThephaseofistobethephaseof

Itisassumedthatthenoiseisstableandthe

powerspectrumofsilenceisusedtoestimate

theillustrativediagramofspectralsubtractionbasicspectralsubtractionbasicspectralsubtractioncleanspeechnoiseyspeechfilteredspeechspectrogramofnoisyspeechspectrogramofcleanspeechspectrogramoffilteredspeechYoucanhearmusicalnoiseobviously.

Ithasgreatinfluenceonlistening.basicspectralsubtraction

basicspectralsubtraction

improvedspectralsubtractionSpectralSubtraction1improvedspectralsubtraction

filteredspeechimprovedspectralsubtractionspectrogramofnoisyspeechspectrogramofcleanspeechspectrogramoffilteredspeechα=1，β=2SpectralSubtractionWienerFilterKalmanFilter123Conclusion4basicwienerfilterIteratingwienerfilter2WienerFilterFFTandsquare

Itisassumedthat

istheexpectationbasicWienerfilterbasicWienerfilter

Thecurrentframewithspectralsubtracion

isusedtomakeupwienerfilter.basicWienerfilterfilteredspeechYoucanstilllistentomusicalnoise.Soit‘sstillspecralsubtractioninalgorithm.That'swhyyoucanhearslightlymusicalnoise.basicwienerfilterIteratingwienerfilter2WienerFilterIteratingwienerfilterForimprovingthefilteredspeech,inotherwordsreducingtheInterferenceofmusicalnoise,weusethefrontaltomakeupthecurrentwienerfilter.IteratingwienerfilterfilteredspeechIteratingwienerfilterWecanseeThemusicalnoiseislower.However,thereisalittlenoiseatthebegainningofthespeechowingtofilter.SpectralSubtractionWienerFilterKalmanFilter123Conclusion4RudolfEmilKalmanMathematicianinHungaryBS&MSatMIT（1953-1954）PhDatColumbia（1957）thepublishmentof《ANewApproachtoLinearFilteringandPredictionProblems》in1960KalmanfilterKalmanfilterisamethodtointroducetheestimatedvalueofthecurrentstate，basedontheestimateofthepreviousstateandobservationsof

thecurrentstate

S(t)=f(S(t-1),O(t))KalmanfilterKalmanfilterSystemofdiscretecontrolprocess:

isthepriorierrorestimateof

,

istheposteriorierrorestimateof.priorierrorestimateandposteriorierrorestimateis：Covariance：KalmanfilterKalmanfilterRecurrenceformulaKisthecorrectionmatrix，alsoknownasBlendfactor.KalmanfilterTheillustrative

diagramKalmanfilterisadataprocessingalgorithmofoptimalrecursive.

optimalmostefficientmostusefultosolvemostproblemsfilteredspeechThenoiseisstillmixedupwiththespeech.KFshouldbethenearoptimalalgorithmtheoretically.It'sprobablyowingtotheprogram.Aseveryonecansee,atthefrontpartoftheprogram,thenoiseallweuseisadditivewhiteGaussiannoise.Now,wewilluseaspeechsegmentwhichmixedwithtrainnoisetotest.improvedsubtractionnoisyspeechcleanspeechfilteredspeechspectrogramofnoisyspeechspectrogramofcleanspeechspectrogramoffilteredspeech

IteratingwienerfilterfilteredspeechkalmanfilterfilteredspeechSNR(Whitenoise)SNR(Trainnoise)SpectralSubtractionWienerFilterKalmanFilter123Conclusion4

Onthisexperimentalconditions,whatwehavedoneistoremovenoises

Gaussianwhitenoise

trainnoiseFirstly,

Spectralsubtractionhasgreateffectonwhitenoise,andimprovedmethod

suppressthemusicalnoise.

Butit'snotgoodatnarrow-bandnoise.Secondly,Wienerfilterhasalmostthesameeffectonthenoise.

Thirdly,Kalmanfilterhasovercometheshortage

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