REALTIMEADAPTIVENOISECANCELLATIONONAFPGA

AshwinKarthikTamilselvan(at3103) RishikanthChandrasekaran(rc3022)GikkuStephenGeephilip(gg2624) RichaGlennNetto(rn2388)

1. OverviewOur project involves implementation of an adaptive noise canceller that takesenvironmentalnoiseasaninputandreducesitinreal-time.Externalsoundisubiquitous;an importantrequirementofcertainenvironmentssuchasvehiclesandaircrafts, is tominimizethenoiseexternaltothesysteminconsideration.Wehaveimplementedourprojectusinganadaptivefilterbasedonthefast-LMSalgorithmandinterfacedtheFPGAwithhardwareperipherals,amicrophoneandaspeaker,tocapturethenoiseandplaythereducednoiseresultrespectively.WehaveutilizedtheaudiocodecontheFPGAtoprocessthesoundsignals.

2. GeneralDescription

a. AdaptiveNoiseCancellation

Noisecancellationhasalwaysbeenoneofthemostfascinatingandconsumermarket-drivenareaofresearch.Inanidealsetup,anefficientcommunicationisoneinwhichthecompletemessageproducedfromthesourceisreproducibleinthedestination.Anidealsetupwouldbeexpectedtobefreeofanyextranoisethatcoulddisruptthecommunicationleadingtothelossofapartofthemessageorthecompletemessagetransmitted.Butanidealsetupisverydifficulttoachievepractically.Communicationhasbeenanintegralpartofthehumanlifeandhavebeenutilizedinvariousforms.Mediatransmissionisonesuchwidelyusedtypeofcommunication.

A special case of audio transmission has been discussed in this project. NoiseCancellation is a technique developedwith an aim of providing the best possibletransmissionofaudiosignalswithoutconsiderablelossofdateordisruptionoftheoriginal signal.Noisesarean inherentlypresentaspartof theenvironment.Noisecancellationintheorywouldaidinthereplication/reproductionoftheaudiosignalinthesamestate, itwastransmittedfromthesource.Varioustechniqueshavebeendevelopedovertimeandadaptivenoisecancellationisapopulartechniqueusedinthemarket.

AdaptiveNoiseCancellationprimarilydealswiththegenerationofanti-noisewhichintheory would cancel out the ambient noise. Adaptive noise cancellation can bebroadly divided into three different setups depending on the position ofmicrophone/s,witheachsetuphaving itsadvantageover theother - FeedforwardNoiseCancellation,FeedbackNoiseCancellationandHybridNoiseCancellation.

WehaveselectedFeedforwardNoiseCancellationtorealizetheworkingofAdaptiveNoiseCancellationonaSoCkitCycloneVboard.Inthissetup,amicrophoneisplacedoutsidetheearcupofaheadphonegivingtheadvantageofextraresponsetimeovertheFeedbackNoiseCancellationsetup.ThisimplementationofANCfaresbetteratreducinghighfrequencynoiseupto1-2kHz.Wehaveusedasinglemicrophoneandanaudiooutput(speakers/headphones)inoursetup.Wehavemodelledthesystemsuchthatthemicrophonefeedstheenvironment/ambientnoiseastheinputtothesystemanda reducednoise is obtainedat theoutput. The anti-noiseproduced issuperimposedontheinputnoisesignal.Thisleadstothepartialcancellationofthenoisesignalresultinginareducednoisesignal.

b. LeastMeanSquareAlgorithm:

WehaveusedtheLeastMeanSquareAlgorithminthisproject,mainlyowingtoitscomputationalsimplicityandeaseofimplementation.Thisalgorithmisbasedontheunderlying concept of Least Mean Squares and the Steepest Descent Algorithm,however,iteliminatestheneedforexactmeasurementsofthegradientvectoranddoesnotinvolvematrixinversion.TheLMSalgorithmisaclassofadaptivefilterusedto mimic a desired filter by adjusting the filter coefficients in such a way that itproducestheleastmeansquareoftheerrorsignal,ie,ifweconsidertheerrortobea cost function, the LMS algorithmbasically finds filter coefficient values that canminimizethecostfunction.Theerrorsignalinthiscaseisthedifferencebetweenthedesired and actual signal. Our project implements an adaptive filter using amoreefficientvariationoftheLMSalgorithmtoupdatetheweightsofthefilterinreal-timewhilesimultaneouslyensuringthatthecomputationalloadontheFPGAisreduced.

FigX:BlockDiagramofAdaptiveFilterusingLMSAlgorithm ThesignalsshownintheFigXaredescribedasfollows:

i. u(k)istheinputvector(purenoisesignal)ii. d(k)isthereferencesignal,ie,thecontaminatedinputsignaliii. y(k) is the output of the adaptive filter, which gives us anti-noise or

reducednoiseiv. e(k)istheestimatederrorobtainedbysubtractingtheanti-noisefromthe

contaminatedsignal.Thisisessentiallythefinalreduced-noiseoutputofthesystem.

ThefollowingequationsdefinetheworkingoftheLMSalgorithm:

i. FilterOutput:y(k)=uTWwhere,W=vectorofweightsappliedtothefiltercoefficients T=transposeoperation

ii. EstimatedError:e(k)=d(k)–y(k)iii. WeightUpdate:Wk+1=Wk+2*μ*e(k)*uk

Theestimatederrorand the inputnoisevectorareappliedas feedback to theadaptiveprocedure.μisthestepsizeandisinverselyproportionaltothesettlingtimeconstantoftheconvergencebehavior.Forsmallervaluesofstepsize,theadaptiveprocessslowsdownbutthemean-squareerrorisminimized,ie,ittakeslongertoconvergebuttheresultsarebetter.Similarly,forlargervaluesofstepsize,theadaptiveprocessconvergesquickly,buttheresultsarenotasgoodasthoseobtainedwithasmallerstepsize.

c. Fast-LMSAlgorithm

Inoursystem,theadaptivefilterisimplementedusingtheFast-LMSAlgorithm.Itfollowsthefollowingequationforupdatingtheweights:

Wk+1=Wk+e(k)*sign(u(k))>>n

The Fast-LMS algorithm replaces step size with a shift operation, where nrepresents the number of shifts. Also, Fast-LMS only uses the sign bit of thereferenceinput,u(k),insteadofusingitsvalue.

FigX:

d. AudioCodec:

TorecordandplayaudioontheFPGA,wehavetobuildan interface to theaudiocodec.Thisbidirectional interfacewillenableustocaptureaudiofromandstreamaudiotothehardwareperipherals,suchasmicrophonesandspeakers.Whensoundtravelsthroughtheairmedium,themicrophoneperceivesitaspressurewavesthatcreatealternatingareasofcompressionandrarefactionasittravelsfromthesourcetothemicrophone.Themicrophonethenconvertsthepressurewavesintoanelectricalsignal,usingavoltageleveltorepresentthepressureatapointinthewave.Thevoltagelevelsarecontinuousintime,sonowwehaveanalogsignals,whichtheFPGAcannotprocesssinceitacceptsdigitalquantitiesandrunsatafixedclockrate. Theaudio codecon the FPGA converts the analog voltage values todiscretequantitiesbycapturingvoltagelevelsatregularintervalsbasedonasamplingrate,and then performing analog-to-digital conversion (ADC) to discretize the capturedvoltagelevels.Inthereverseprocess,whenwewanttoplayanaudiosignalfromtheFPGAthroughthespeakers,thecodecperformsdigital-to-analog(DAC)conversion,which results in a slight lossof informationdue to reconstructionof a continuoussignalfromabunchofdiscretevalues.The audio codec on this FPGA (Analog Devices SSM2603) is configured withparameterssuchassamplingrateandsamplingwidthusingtheI2Cprotocol.

3. Setup

a. AdaptiveNoiseCanceller–PhysicalSetup

The setup of our project requires one microphone, one speaker and a noisesource.Weareusingawhitenoisesourceplayedoutofacellphoneasthenoisyinput.Themicrophoneisheldnearthenoisysourceandispluggedintothemic-inportontheFPGA.Theline-outportonFPGAisconnectedtoaspeakeranditgivesthereducednoiseasanoutput.

b. Fast-LMS

WehavedecidedtouseFast-LMSinsteadofLMSforourprojectbecauseFast-LMSonlyusesthesignbitoftheinputintheformulawhereweightsareupdatedandhence, it significantly reduces the number of multiplications required andsimplifiestheimplementationoftheLMSfilterinhardware.Italsoreplacesstepsizewithashiftoperation,furthersimplifyingthealgorithm.

WehavemodifiedtheblockdiagramofAdaptiveNoiseCancellationtoaccountfor the fact that our input to the system is noise (in the inherent presence ofsilence)recordedthroughamicrophone.TheAdaptiveFilteralsogetsthesamenoisyinput,anditgeneratesareducednoisewhichisrepresentedinthediagramas Filter Output. Then, we subtract the Filter Output from the Noisy Input togeneratetheerroroutput,whichisthereducednoisesignal.Thisisthensentbackto theadaptive filter so that it canbeused toupdate theweightsof the filtercoefficients.Finally,thereducederroroutputissentoutviaaspeaker.

FigX:

c. AudioCodec

TheSSM2603AudioCodectakes16-bitdatawords.Eachtransmissionwillinclude3 acknowledgements before the stop symbol must be sent. The maximumfrequencyofSCLKis526kHz.SCLKisdivideddownby128,givingafrequencyof

about 390 kHz. The codec only checks for acknowledgements after all 24-bits(includingaddress,dataandr/wbit)aretransmitted.Theacksignalissethighifalltheacknowledgementshavebeenreceived.UsingthedatasheetoftheSSM2603AudioCodec,wehaveassignedvaluestotheregisters that we require in our system. The audio codec organizes itsconfiguration variables into 19 9-bit registers. The first seven bits of thetransmitteddataareregisteraddress,thelast9bitsaretheregistercontents.

Register RegisterName

Value

R0 LeftChannelADCInputVolume

000010111

R1 RightChannelADC

InputVolume

000010111

R2 LeftChannelDACVolume

001111001

R3 RightChannelDAC

Volume

001111001

R4 AnalogAudioPath

011010100

R5 DigitalAudioPath

000000100

R7 DigitalAudioInterface

000000001

R8 SamplingRate

000100000

4. Implementation

WedevelopedthesystemmodelwithSimulinkandevaluatedthemeasureddatausingMATLAB.WethenusedModelSimtoverifytheresultsofthehardwaresimulationoftheadaptivefilterusingFast-LMSalgorithmandplotte dtheobtainedoutputusingMATLABtoverifythenoiseattenuation.ThefinalsynthesisoftheSystemVerilogcodehasbeendoneontheprovidedSockitCycloneVFPGA.

a. SimulinkandMATLABTheLMSalgorithmwasmodelledonSimulinkandMatlabtoverifyoperation.Wealsousedmatlabtoiterateoverthedesignofthealgorithmtomakeitmoreefficient.WemathematicallymodelledthealgorithmandreducedthecontrolblockdiagramstomakeitsuitableforimplementationontheFPGA.WethentestedthedesignedalgorithmwithactualaudioinputfromamicinterfacedviaUSBandplayedbacktheoutput.TheSimulinkblockdiagramdesignedisshownbelow:

b. ModelSimTheRTLorhardwaredesignwasdoneinSystemVerilogandfirstsimulatedonModelSim.Astatemachinewasdesignedtotakecareofdifferentsequenceofprocessingsinvolved.Thestatemachinehas6states.Thestatemachinetransitionsoccuronthepositiveedgeofthemainclockwhichrunsat50MHz,butthetriggeringofthestatemachineisonthepositiveedgeoftheDACclock(theIdlestatewaitsforthepositiveleveloftheDACclock).Thefirststate:WaitsfornoiseinputsampleSecondState:ComputefilterweightsThirdState:StorenoiseinputincircularbufferFourthState:IncrementbaseaddresspointerofcircularbufferFifthState:RestoreprevioushistoryofnoisesamplefromcircularbufferforeachtapSixthState:IdleState

Theerrorvaluesarecalculatedusingcontinuousassignmentstatementswhichcomputersthevaluesinstantlywhenanyofitsinputchanges.TheoutputvaluesfortheDACarewrittenatthenegativeedgeoftheDACclocksothatthedataisreadyforpushingtotheDACintheaudiocodecduringthenextimmediateclockcycle.WerecordeddatafromamicviaMATLABhaditconveredtointegersamplestofeedintotheModelSImsimulationinrealtime.AtestbenchwasalsodesignedinsystemVerilogwhichfeedsinputgeneratedasmentionedaboveintothedesignedSystemVerilogmoduleinreal-timeaccordingtotherequiredclock.Italsorecordstheoutputtoafileinreal-time.TheoutputdateisthentakenoutandplottedinMatlabtocheckthefunctioning.Theplotisshownasseenbelow:

5. ChallengesandLessonsLearnt:CHALLENGES:a.FiguringoutAudioCodec:Extensivereadingofthedatasheettofigureoutthe differentmodesandconfigurationregistersb.ImplementingI2CcommunicationfortheAudioCodecc.LMSoptimizationandreduction:SpentweekspouringoverresearchpapersandmathtofigureoutdifferentoperationsandhowwecouldreducethemtoenablefasterconvergenceaswellasreducemultiplicationstomakeiteasiertoimplementonFPGA.d.Findanalternativetofloatingpoint:Ideallytheweightsarefloatvalueswhichprovidebetterfilterperformance.SincetheSoCKitAlteraFPGAdoesnothaveafloatingpointunit,wehadtouseonlyintegerwaitsandhadtofindawaytoworkwiththat.e.IntegratingtheAudioCodecandtheLMSmoduletogetherforreal-timeperformanceLESSONSLEARNT:

a. Designingreal-timeembeddedsystemsrequiresagoodknowledgeofclocklevelexecutionofstatements.

b. Clockisgod.c. Timemanagement

6. FutureWork:ThesamesetupcanbeusedwithamoreoptimizedversionofthefastLMSalgorithmoramoreoptimizedalgorithmtoprovidebettercancellationeffect.Themodeltakesinambientnoiseasaninputandproducesareducedversionofthenoisesignalusingasinglemicrophoneandasingleaudiooutput.Inthecurrentsetup,silenceisinherentlymixedwiththeambientnoisesignaltoformtheinputofthesystem.Thissystemcanbeextendedtotakeanyaudiosignalmixedwithanambientnoiseandprovideanoisereduced/cancelledaudiosignalastheoutput.Anenhancementtoprovidesupportformp3formataudiofilescanbeaddedtoimprovetheflexibilityandextendtheoperationdomainofthesystem.

7. Contribution:a.SystemDesign:Richa,Gikkub.LMS:AlgorithmDesign:Richa,GikkuFPGA:Gikku,Rishic.AudioCodec:Rishi,Ashwin

d.Presentation:Rishi,Ashwind.Report:Richa

REFERENCES:[1]http://www.ti.com/lit/an/spra095/spra095.pdf[2]Foul,Wolfgang,JörnMatthies,andBerndSchwarz."AFPGA-basedadaptivenoisecancellingsystem."Proc.ofthe12thInt.ConferenceonDigitalAudioEffects(DAFx-09).2009.[3]Software/HardwareImplementationofanAdaptiveNoiseCancellationSystem”Dr.WagdyHMahmoud,Dr.NianZhang,UniversityoftheDistrictofColumbia[4]https://people.ece.cornell.edu/land/courses/ece5760/FinalProjects/f2011/jy554_jc2636/jy554_jc2636/_fpgaimplementation.htm[5]http://blog.jabra.com/anc-headsets-arent-all-the-same-three-types-of-anc/[6]http://www.analog.com/media/en/technical-documentation/data-sheets/SSM2603.pdf[7]SoCKitUserManual