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基于非负字典训练和鲁棒主成分分析的语音增强算法（英文）.docx

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基于非负字典训练和鲁棒主成分分析的语音增强算法（英文） Title:SpeechEnhancementAlgorithmBasedonNon-negativeDictionaryTrainingandRobustPrincipalComponentAnalysis Abstract: Speechenhancementplaysacrucialroleinimprovingthequalityofspeechsignalscontaminatedbynoise.Inthispaper,weproposeaspeechenhancementalgorithmthatcombinesnon-negativedictionarytrainingandrobustprincipalcomponentanalysis(PCA)techniques.Thealgorithmaimstoeffectivelyreducethenoisewhilepreservingthespeechsignal'sintelligibilityandnaturalness. 1.Introduction Speechsignalsareoftencorruptedbyvarioustypesofnoise,suchasbackgroundnoise,roomreverberation,andchanneldistortions.Thesenoisecomponentsdegradethequalityandintelligibilityofthespeechsignal,makingitchallengingtodecodeorunderstandtheinformation.Therefore,speechenhancementalgorithmsarecrucialinapplicationssuchasspeechrecognition,telecommunication,andhearingaids. 2.Non-negativeDictionaryTraining Non-negativedictionarytrainingisapopulartechniqueforspeechenhancement.Itaimstolearnasetofbasisvectorsthatrepresentthespeechsignalandnoisecomponentsseparately.Thedictionaryistrainedtoensurethatthecoefficientsofthespeechsignalandnoisearenon-negative.Inthisway,thealgorithmexploitsthenon-negativitypropertiestoseparateandsuppressthenoisecomponentseffectively. 3.RobustPrincipalComponentAnalysis RobustPrincipalComponentAnalysis(PCA)isanotherwidelyusedtechniquefordataanalysis,includingspeechenhancement.PCAdecomposestheinputsignalintoalow-rankcomponent(speechsignal)andasparsecomponent(noise).Thelow-rankcomponentrepresentstheunderlyingstructureofthespeechsignal,whilethesparsecomponentcapturesthenoiseoroutliercomponents.Byextractingthelow-rankcomponent,itispossibletoenhancethespeechsignalwhilesuppressingthenoise. 4.ProposedAlgorithm Inourproposedalgorithm,wecombinetheadvantagesofnon-negativedictionarytrainingandrobustPCAtoenhancespeechsignalscontaminatedbynoise.Thealgorithmcanbesummarizedasfollows: Step1:Preprocessing Theinputsignalisdividedintoshortframesusingawindowingtechnique,suchastheHammingwindow.Eac