Speech Enhancement Techniques for Digital Hearing Aids

162 Pages·2019·11.981 MB·English

by  Komal R. Borisagar, Rohit M. Thanki, Bhavin S. Sedani

Komal R. Borisagar · Rohit M. Thanki 
Bhavin S. Sedani
Speech
Enhancement
Techniques for
Digital Hearing
Aids
Speech Enhancement Techniques for Digital
Hearing Aids
(cid:129)
Komal R. Borisagar Rohit M. Thanki
Bhavin S. Sedani
Speech Enhancement
Techniques for Digital
Hearing Aids
KomalR.Borisagar RohitM.Thanki
E.C.Department C.U.ShahUniversity
AtmiyaInstituteofTechnology WadhwanCity,Gujarat,India
andScience
Rajkot,Gujarat,India
BhavinS.Sedani
E.C.Department
L.D.EngineeringCollege
Ahmedabad,India
ISBN978-3-319-96820-9 ISBN978-3-319-96821-6 (eBook)
https://doi.org/10.1007/978-3-319-96821-6
LibraryofCongressControlNumber:2018952353
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Preface
Theinterferenceofbackgroundnoiseisthegreatestproblemreportedbyhearingaid
wearers. Background noise is a combination of different frequencies; these
unwantedfrequenciesreducetheprecisionofspeech.Ahigherlevelofbackground
noise degrades intelligibility. The speech signal is a quasi-periodic signal. If the
speech signal is masked by noise, then the intelligence of that signal is reduced
significantly and because of that, it is difficult to understand what is being said.
Moreover,thespeechsignalcarriesmanyredundantsampleswhichmasksaportion
of the noise. Noisy signal can’t be easily detected by a person with a hearing
disorder.It’sanirritatingprocessbetweendetectingintelligencefromanoisyspeech
bythosewithhearingdisorderswhereasnormalpeoplecandothisjobeasily.
Apersonwithahearingimpairment,mayfinditcumbersometoidentifyspecific
frequenciesinthepresenceofnoisethatcontainsbasicfrequencies.Inthatcase,the
speechsignalisnotaudible;thus,itisnotpossibletointerpretspeech,andthismay
cause problems in everyday life. Any product associated with hearing aids needs
more work on design in such a way that the effect of the noise would be reduced
before any further modification. It is vital to observe the sound quality with the
backgroundnoise.
Deafnessisaconicaldisabilityduetoeitherasensoryneuraldefectinwhichcells
are dead because of age, or a major disease. Deafness can also be caused by
problems with bone and air conduction. One of the methods to solving hearing
disordersisbycochlearimplants,butthisrequiressurgery.Asanalternative,most
people wear hearing aids instead. Enhancement of noisy speech is possible in the
caseofhearingaids.Generally,differentnoiseshaveaffectedvariousfrequenciesof
thespeechsignal.Fixedfilterscanhelpagreatdealwhenitcomestoremovingan
unwanted noise frequency. However, there can be many variations of noise fre-
quency and over time it may degrade the fixed filter. It can be seen that with an
unwantedsignal,thespeechcomponentisalsoaffected.Withthatconstraint,theuse
of filtering techniques that are only applicable to the incoming noise signal is
required. In addition to this, as per the noise characteristics, the filtering process
shouldbeadaptive.
v
vi Preface
Even after the removal of noise from speech using advance adaptive filtering
methods, most partially deaf patients are not able to recognize all the frequencies
equally.Thefrequencyresponseofthepatient’seargivesproperinformationabout
lossesataparticularfrequency.Basically,speechwiththenoiseremovedshouldbe
enhancedaspertheaudiogramstructureoftheindividual.Cleanspeechisthename
given to the enhancement in the individual frequency band in the wavelet domain.
The audiogram requires individual frequency components that are sometimes less
sensitive;thusonlybandswhichareamplifiedbyvolumecanprocessspeechusing
the multi-resolution approach of a discrete wavelet transform. This book presents
novelapproachessuchas:adaptivefilteringandfrequencybandenhancementinthe
waveletdomainthatassistspeechenhancementaswellasnoisereductioninspeech
signals.
Thebookexplainshownoisecanbeextractedfromthesilentpartofthespeech
signal. The various types of adaptive filter such as the least mean square (LMS),
normalized LMS (NLMS), and the recursive least square (RLS) are described for
noise reduction from the noisy speech signal along with voice activity detection
(VAD).Thedifferenttypesofpracticalpossiblenoisesgeneratedforthepreparation
ofnoisyspeechsignalandspeechcanbecleanedeffectively.Predictionofthenoise
signal is the main task in the present work. The methods mentioned are compared
basedonvariousparameterssuchastheamountofnoisereduction,theestimationof
filterweightsandtheconvergencerateofthefilters,meansquareerror(MSE),and
peaksignaltonoiseratio(PSNR).Thepresentedmethodsprovidesignificantresults
in noise reduction and can be observed within a time domain. Also, reconstructed
speechcanbeobservedwithnoiseremovalandproperintelligence.
ThisbookisaPh.D.researchworkandextensionworkofDr.KomalBorisagar,
submitted to the Department of Electronics and Communication Engineering, Shri
Jagdish Prasad Jhabarmal Tibrewal University (JJTU), Jhunjhunu, Rajasthan in
2012. The authors are indebted to numerous colleagues for valuable suggestions
duringtheentireperiodofthemanuscript’spreparation.Wewouldalsoliketothank
thepublishersatSpringer,inparticularMaryE.James,seniorpublishingeditor/CS
Springer, for their helpful guidance and encouragement during the creation of
thisbook.
Rajkot,Gujarat,India KomalR.Borisagar
WadhwanCity,Gujarat,India RohitM.Thanki
Ahmedabad,India BhavinS.Sedani
Contents
1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Sound. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 EarStructureandItsWorkings. . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2.1 ExternalEar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 MiddleEar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.3 InnerEar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.4 Cochlea. .. . . .. . . .. . . .. . . .. . .. . . .. . . .. . . .. . . .. 3
1.3 HearingImpaired. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Audiogram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.5 DigitalHearingAids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.6 IssuesinDigitalHearingAids. . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.7 MotivationforThisBook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.7.1 ImportantAreasofSpeechSignalCovered
inThisBook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.8 BookOrganization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 GenerationofSpeechSignalandItsCharacteristics. . . . . . . . . . . . . 13
2.1 SpeechSignal. .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . 13
2.1.1 ArticulatoryPhoneticsandSpeechGeneration. . . . . . . . . . 13
2.1.2 AnatomyandPhysiologyofSpeechGeneration. . . . . . . . . 14
2.1.3 VocalTract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1.4 LarynxandVocalFoldsorCords. . . . . . . . . . . . . . . . . . . 16
2.2 MajorFeaturesofSpeechArticulation. . . . . . . . . . . . . . . . . . . . . 18
2.3 PropertiesandCharacteristicsofSpeechSignal. . . . . . . . . . . . . . 20
2.3.1 TimeandFrequencyDomainCharacteristics
ofSpeech. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.2 Waveforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.3 FundamentalFrequency. . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.4 OverallPower. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
vii
viii Contents
2.3.5 OverallFrequencySpectrum. . . . . . . . . . . . . . . . . . . . . . 22
2.3.6 Short-TimeEnergy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3.7 Spectrogram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3.8 Short-TimeAverageZeroCrossingRate. . . . . . . . . . . . . . 24
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3 IntroductionofAdaptiveFiltersandNoisesforSpeech. . . . . . . . . . . 29
3.1 AdaptiveFilter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 LMSAdaptiveFilter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.1 LeastMeanSquareAdaptationAlgorithm. . . . . . . . . . . . . 33
3.2.2 StatisticalLMSTheory. . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.3 DirectAveragingMethod. . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.4 SmallStepSizeStatisticalTheory. . . . . . . . . . . . . . . . . . . 37
3.2.5 NaturalModesoftheLMSFilter. . . . . . . . . . . . . . . . . . . 38
3.2.6 LearningCurvesforAdaptiveAlgorithms. . . . . . . . . . . . . 39
3.2.7 ComparisonoftheLMSAlgorithmwiththeSteepest
DescentAlgorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3 NormalizedLeastMeanSquare(NLMS)AdaptiveFilter. . . . . . . . 41
3.3.1 StructureandOperationofNLMS. . . . . . . . . . . . . . . . . . 42
3.3.2 StabilityoftheNormalizedLMSFilter. . . . . . . . . . . . . . . 45
3.3.3 SpecialEnvironmentofRealValuedData. . . . . . . . . . . . . 46
3.4 RecursiveLeastSquares(RLS)AdaptiveFilter. . . . . . . . . . . . . . . 48
3.4.1 Regularization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.4.2 ReformulationoftheNormalEquations. . . . . . . . . . . . . . 50
3.4.3 RecursiveComputationsofФ(n)andz(n). . . . . . . . . . . . . 50
3.4.4 TheMatrixInversionLemma. . . . . . . . . . . . . . . . . . . . . . 51
3.4.5 SelectionoftheRegularizationParameter. . . . . . . . . . . . . 53
3.4.6 ConvergenceAnalysisofRLSAlgorithm. . . . . . . . . . . . . 54
3.4.7 ConvergenceoftheRLSAlgorithminthe
MeanValue. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.4.8 MeanSquareDeviationoftheRLSAlgorithm. . . . . . . . . . 56
3.4.9 EnsembleAverageLearningCurveofthe
RLSAlgorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.5 Noise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.5.1 SourcesofNoise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4 FourierTransform,Short-TimeFourierTransform,
andWaveletTransform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1 FourierTransform(FT). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 Short-TimeFT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.3 WaveletTransform(WT). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.4 ComparisonoftheWaveletTransform(WT)
withFTandSTFT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.5 MultiresolutionAlgorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Contents ix
5 SpeechSignalEnhancementUsingAdaptiveFilters. . . . . . . . . . . . . 75
5.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2 StepsforSpeechEnhancementProcess. . . . . . . . . . . . . . . . . . . . 76
5.3 ImplementationFlowofVADAlgorithm. . . . . . . . . . . . . . . . . . . 76
5.4 SpeechEnhancementProcessbasedonLMSAlgorithm. . . . . . . . 77
5.4.1 ResultsforWhiteNoiseSignal. . . . . . . . . . . . . . . . . . . . . 81
5.4.2 ResultsforBabbleNoiseSignal. . . . . . . . . . . . . . . . . . . . 86
5.4.3 ResultsforTrafficJamNoiseSignal. . . . . . . . . . . . . . . . . 91
5.5 SpeechEnhancementProcessBasedontheNLMSAlgorithm. . . . 95
5.5.1 ResultsforWhiteNoiseSignal. . . . . . . . . . . . . . . . . . . . . 98
5.5.2 ResultsforBabbleNoiseSignal. . . . . . . . . . . . . . . . . . . . 102
5.5.3 ResultsforTrafficJamNoiseSignal. . . . . . . . . . . . . . . . . 106
5.6 SpeechEnhancementProcessBasedontheRLSAlgorithm. . . . . . 109
5.6.1 ResultsforWhiteNoiseSignal. . . . . . . . . . . . . . . . . . . . . 110
5.6.2 ResultsforBabbleNoiseSignal. . . . . . . . . . . . . . . . . . . . 115
5.6.3 ResultsforTrafficJamNoiseSignal. . . . . . . . . . . . . . . . . 119
5.7 ComparativeAnalysisofSimulationResults.. . . . .. . . . .. . . . .. 123
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
6 SpeechSignalEnhancementBasedonWaveletTransform. . . . . . . . 125
6.1 ProcedureforSpeechSignalEnhancementUsing
WaveletTransform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6.2 ImplementationandResultsofSpeechSignal
EnhancementUsingWaveletTransform. . . . . . . . . . . . . . . . . . . . 130
6.2.1 FirstBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . . . 131
6.2.2 SecondBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . 133
6.2.3 ThirdBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . . . 134
6.2.4 FourthBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . . 137
6.2.5 FifthBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . . . 138
6.2.6 SixthBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . . . 140
6.2.7 SeventhBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . 142
6.2.8 EighthBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . . 143
6.2.9 NinthBandEnhancement. . . . . . . . . . . . . . . . . . . . . . . . . 145
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
7 SummaryofThisBookandFutureResearchDirections. . . . . . . . . . 149
7.1 ImportantPointsCoveredintheBook. . . . . . . . . . . . . . . . . . . . . 149
7.2 FutureResearchDirection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Chapter 1
Introduction
1.1 Sound
A sound wave is described as being similar to ripples on the surface of the sea. A
“wave” in the atmosphere contains much less variation in pressure than normal
atmospheric variations. An acoustic or sound wave as a source generates the
compressionandrarefactionofparticles.Usually,soundisgeneratedbyavibrating
entity such asaviolin string, aloudspeaker diaphragm, themotorinamachine,or
the vocal cords in a human body. Also, sound cannot travel in a vacuum. The
eardrum vibrates in direct response to pressure variations in a wave when they
reachtheear,andthepressurefluctuationsareheardasasound.
1.2 Ear Structure and Its Workings
Human ears arefound in pairs, situated on theleft and right sides ofthe head, and
also can be considered as sensory organs comprising the auditory system, which
detectssound,andthevestibularsystem,whichisresponsibleformaintainingbody
balance and equilibrium [1]. The basic structure of the ear is shown in Fig. 1.1
[1].Theearcanbedividedintothreepartsanatomically,knownastheexternalear,
middle ear,and inner ear.The sound collectionand amplifyingmechanisms ofthe
eararesuchthat
(cid:129) Itcanworkasatransducer,whichconvertssoundvibrationintoactionpotentials.
(cid:129) Theactionpotentialcanbedeliveredbythenerves.
©SpringerNatureSwitzerlandAG2019 1
K.R.Borisagaretal.,SpeechEnhancementTechniquesforDigitalHearingAids,
https://doi.org/10.1007/978-3-319-96821-6_1