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BIAS STANDARDS FOR SIGNAL PROCESSING
A IMS, PROCESSES AND RECOMMENDATIONS
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Coverpicture:Soundpressurelevel(oneminuteaverage)vs.distancetotheclosestshipattheBIASlocation12(BothnianBay).Thefiguresshowthatthenoiseofashipcloserthan5–10kmcanbedistinguishedfromthebackgroundnoise.ByJukkaPajala.
BIAS-BalticSeaInformationontheAcousticSoundscape
TheBalticSeaisasemi-enclosedseawithnineborderingstates.Itconsistsof8sub-catchmentareas(sub-basins)andanumerousofharbours.Theshippingdensityisoneofthehighestintheworld.Itisestimatedthatabout2000sizeableshipsareatseaintheBalticSeaatagiventime.Besidesshipping,existingandplannedwindfarmscontributetotheambientnoiseofthesewaters.
InSeptember2012,BIASstarted.ThisprojectisfundedbyEULIFE+andhasthreemainobjectives.ThefirstobjectiveistoestablisharegionalimplementationofDescriptor11oftheMarineStrategyFrameworkDirectivethroughunderwatersoundmeasurementsthroughouttheBalticSeaandthedevelopmentofuser-friendlytoolsformanagementoftheDescriptor.Thesecondobjectiveistoestablishregionalstandardsandmethodologiesthatwillallowforcross-borderhandlingofdataandresults,whichisnecessaryforanefficientjointmanagement.ThethirdobjectiveistousethemeasurementstomodelthesoundscapeoftheentireBalticSea.
BIASwillsolvethemajorchallengeswhenimplementingDescriptor11intheBalticSea.Intotal38sensorsweredeployedthroughouttheBalticSeain2014tomeasurethenoiselevelsduringtheentireyear.Themeasurementswereperformedbyfollowingthestandardsdocumentedinthisreport.Likewisewerethedataanalysedusingstandardizedsignalprocessingroutines.Resultsweresubjecttoaqualitycontrolandfinallystoredinacommondata-sharingplatform.
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Thepresentsignalprocessinginstructionwasdrawnupwithinthescopeoftheresearchproject„BalticSeaInformationontheAcousticSoundscape”.Thisreportcanbecitedasfollows:BetkeK.,FolegotT.,MatuschekR.,PajalaJ.,PerssonL.,TegowskiJ.,Tougaard,J.,WahlbergM.(2015).BIASStandardsforSignalProcessing.Aims,ProcessesandRecommendations.Amendedversion.2015.Editors:VerfußU.K.,SigrayP.
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Contents A.Introduction...................................................................................................................5
B.Scope..............................................................................................................................6
C.Termsanddefinitions.....................................................................................................6
D.AimofBIASsignalprocessing.........................................................................................8
E.Standardsonsignalprocessing........................................................................................9I.Qualitycontrolofdata.............................................................................................................................................9II.Pre-processing........................................................................................................................................................11III.Processing...............................................................................................................................................................13IV.Presentationofresults.......................................................................................................................................16V.DataManagement..................................................................................................................................................21
F.References....................................................................................................................27
G.Appendix......................................................................................................................28I.ShortdescriptionofMatlabscriptsforBIAS...............................................................................................28
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A. Introduction Theambientnoiselevelsintheoceanshasincreasednoticeableoverthepast50yearsduetoincreasedanthropogenicactivitiessuchasshippingandmanytypesofoffshorework.SinceWorldWarII,theaverageleveloftheambientnoiseincreasedby12-15dB(Bjørnø,1998).Soundcanpotentiallyhavenegativeeffectsonmarineorganismsbydisruptingvitalbehavioursorphysiologicalprocesses,inextremecasesevenleadingtotheirdeath(e.g.Fernandezetal.,2005).TheEuropeanUnionthereforeregardstheintroductionofsoundenergyasoneofthethreatstothemarineenvironmentthatrequiresanEUwidecooperativeactionandregulation.ThisisadriverfortheMarineStrategyFrameworkDirective(MSFD),adoptedbytheEuropeanUnioninJuly2008.ThemaingoaloftheMarineDirectiveistoachieveaGoodEnvironmentalStatus(GES)ofEUmarinewatersby2020.Withregardstounderwatersound,Descriptor11oftheMSFDstatesthatGESisachievedwhentheintroductionofenergy,includingsound,isatlevelsthatdonotadverselyaffectthemarineenvironment.Forimplementingthis,amonitoringprogrammehastobeestablishedobservingthecurrentlevelandanytrendofambientnoiseinEuropeanseas.Definingacousticparametersandestablishingstandardsfacilitateregionalmarineenvironmentalmanagementofunderwatersoundandguaranteecompatibleandquality-assureddata.
Theinternationalproject“BalticSeaInformationontheAcousticSoundscape”(BIAS)startedinSeptember2012.ThisprojectisfundedbytheEuropeanCommissionundertheLIFE+programandnationalco-funderstoestablisharegionalimplementationofDescriptor11oftheMSFDfortheBalticSearegion.TheBIASsoundmeasurements,conductedthroughouttheBalticSea,willprovideabaselineofthecurrentlyprevailingambientnoiseforfuturemonitoring.Theprojectalsoestablishesregionalstandardsanduser-friendlymethodologiesthatwillallowforcross-borderstandardizedmeasurements,datahandling,signalprocessingproceduresandtheefficientmanagementofDescriptor11.
BIASreleasedastandardfornoisemeasurementsanddatahandlingin2014andarevisedversionin2015(Verfussetal.,2014;2015).Thecurrentdocumententailsastandardfordigitalsignalprocessing,withthescopeandthetermsanddefinitionsgiveninchapterBandchapterC.Thedocumentdescribestheaim(chapterD)andproceduresofsignalprocessingandthestandardsthatwereadoptedwithintheBIASproject(chapterE),includingqualityassurance(chapterE.I),pre-processing(chapterE.II)aswellasprocessingprocedures(chapterE.III),andrecommendationsonhowtopresentresults(chapterE.IV).AcousticdatasetcharacteristicsandformatsusedintheBIASprojectsaregivenintheDatamanagementsection(chapterE.V).ShortdescriptionsoftheappliedMatlabscriptsaregivenintheAppendix.
Theaimofthisreportistoconstitutethebasisforaregionalmonitoringofhuman-introducedunderwaternoiseintheBalticSeainaccordancewithMSFDdescriptor11thatcanserveasadraftforaEuropeanStandard.
TheBIASSignalProcessingStandardispreparedforBalticSeanoisedataandisbasedonthefollowingpreviouslypublishedstandards:
• IEC1995(EN61260),Electroacoustics-Octave-bandandfractional-octave-bandfilters,InternationalElectrotechnicalCommission,Geneva,Switzerland,1996.
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• IEC60565:2006Underwateracoustics-Hydrophones-Calibrationinthefrequencyrange0.01Hzto1MHz,IEC60565-2006(EN60565:2007,BS60565:2007),InternationalElectrotechnicalCommission,Geneva,Switzerland,2006.
• IEC60050:1994,InternationalElectrotechnicalVocabulary,part801:AcousticsandElectroacoustics,(section801-32coverstermsforunderwateracoustics),InternationalElectrotechnicalCommission(IEC),Geneva,Switzerland,1994.
• ISO1996-1:2006,Acoustics–Description,measurementandassessmentofenvironmentalnoise–Part1:Basicquantitiesandassessmentprocedures.InternationalOrganizationforStandardization,Geneva,Switzerland,2006.
• ISO80000-8:2007.Quantitiesandunits–part8:Acoustics,InternationalOrganizationforStandardisation,Geneva,Switzerland,2007.
• ISO/TR25417:2007.Acoustics—Definitionsofbasicquantitiesandterms.InternationalOrganizationforStandardisation(ISO),Geneva,Switzerland,2007.
• ANSI/ASAS12.64-2009/Part1,2009.QuantitiesandProceduresforDescriptionandMeasurementofUnderwaterSoundfromShips-Part1:GeneralRequirements,AmericanNationalStandardInstitute,USA,2009.
• ANSIS1.11-2004:SpecificationforOctave-BandandFractional-Octave-BandAnalogandDigitalFilters,AmericanNationalStandardInstitute,USA,ReaffirmedbyANSI2009.
B. Scope ThisdocumentspecifiesthesignalprocessingproceduresthatwereadoptedbyallpartnersoftheBIASproject(theBIASbeneficiaries).Itisrestrictedtotopicsrelatedtotheanalysisoftheretrieveddigitizedsounddataandthereforeonlydealswithdigitalsignalprocessing.Itdoesnotspecifydataacquisitionprocedures.Thosearepublishedinthe“BIASStandardsforNoisemeasurementsandDataHandling”(Verfussetal.,2014;2015).Foreachsteprelevantintheprocessingofthedata,ageneraldescriptionwillbegiventowhathasbeendoneintheBIASprojectwithsomejustificationtowhyithasbeendone,followedbyadetailedspecificationonthealgorithmsorproceduresandrecommendationsgivenbytheBIASteam.
C. Terms and definitions Thissectiondefinesthetermsasusedinthefollowingtext.
a. 1/3-octave frequency band Afrequencybandwithabandwidthofonethirdofanoctave.Oneoctaveisadoublingoffrequency,andonethirdofanoctaveisafrequencyratioof21/3≈1.26betweenthehighestandthelowestfrequency(adaptedfromRobinsonetal.,2014).
b. 1/3-octave f i l ter Abandpassfilterwithabandwidthof1/3-octave.
c. ANSI AmericanNationalStandardsInstitute.
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d. Arithmetic mean Thesumofseveralmeasurementsdividedbythenumberofmeasurements.
e. Bandwidth Thefrequencyrangewithinwhicharecordingsystemissensitive.
Thefrequencyrange(inHertz)obtainedbysubtractingthelowerfromtheuppercut-offfrequency.
f . Broadband level Thesoundpressurelevelobtainedoverawidefrequencyrangewithdefinedfrequencylimits.
g. Centre frequency Synonymtomidbandfrequency.
Thecentrefrequency(fm)ofabandequalsthegeometricmeanofthelower(flow)andupper(fhigh)cut-offfrequencies:𝑓! = 𝑓!"#𝑓!!"!.Fora1/3octavebandthecut-offfrequenciesaregivenas
𝑓!"# = 2!!!𝑓!and𝑓!!"! = 2
!!𝑓!(amendedfromANSI,2004).
h. Clipping Clippingoccurswhentherecordingsystemissaturatedbeyonditsmaximumsoundpressureorvoltagecapacity.Thisresultsinunderestimationofsoundlevelsaswellasspectraldistortion.
i . Cut-off frequency ( lower / upper) Synonymoustobandedgefrequency.
Frequenciesofthelowerandupperedgesofabandpassfiltersuchthatthecentrefrequencyisthegeometricmeanoftheloweranduppercut-offfrequencies(amendedfromANSI,2004).
j . Discrete Fourier Transform Thedigital(sampled)Fouriertransformoverafinitenumberofsamples(incontrasttotheintegralFouriertransform,whichisappliedtoacontinuous(analog)waveform).Thisalgorithminvolveslongcomputations,butcanbeappliedtosignalsofanylength,incontrasttotheFastFourierTransform(Bloomfield,1976).
k. Estimate Theruleofmethodofestimationiscalledestimator,andthevaluetowhichitgiveriseinaparticulariscalledtheestimate(Poularikas,1999).
l . Fast Fourier Transform AclassoffastalgorithmimplementingthediscreteFouriertransform,thatefficientlycalculatesthediscreteFouriertransformfromthesampledtimewaveform(Bloomfield,1976,2000).
m. Fourier transform Amathematicaloperation,whichtransformsarepetitivesignalintoasumofharmonicsinewavefunctions.Canbeperformedoncontinuous(analog)signals(integralformulation)anddigital(sampled)signals(discreteformulation)(Bloomfield,1976).
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n. Geometric mean Themeanofnpositivenumbersobtainedbytakingthenthrootoftheproductofthenumbers𝑚 = 𝑚!𝑚!…𝑚!
! .
o. IEC InternationalElectrotechnicalCommission.
p. IEC/ISO band number Indexofthe1/3-octavebands,asdefinedbyIEC,ISOandANSI.Centrefrequencyisgivenfromband
numberxas:𝑓! = 10!!",thusbandnumbers0,18and21havecentrefrequenciesof1Hz,63Hzand
125Hz,respectively.
q. ISO InternationalOrganizationforStandardization.
r . Measurand Thequantityintendedtobemeasured.
s. Ring-test Aninter-organizationcomparisonofsignalprocessingmethods,beingatooltoverifythecorrectpracticeindataanalysis.
t . Spectrum Aquantityexpressedasafunctionoffrequency,eitherasanarrowbandspectrum(e.g.1Hzbands)orasaggregatedbands(e.g.1/3-octavebands)(Robinsonetal.,2014).Spectracanbederivedbyvariousmethods,suchasanFFTorfilteringwithasetofband-passfilters.Acriticalrequirementofaproperspectrumisthatthesumofthepowerinallbandsshouldequalthetotalpowerofthesignal.
u. UTC UniversalTimeCoordinated.
D. Aim of BIAS signal processing ThemainroleoftheBIASsignalprocessingisfourfold:
1.Tocontrolthequalityofdata,
2.Toextractrelevantestimatesoutoftherawdataset,
3.Topresentthepropertiesofdatainanunderstandableway,
4.Toprovideresultsinanappropriateformatforsoundscapemodelling.
Long-termacousticrecordingsresultinextremelylargedatasetsthathavetobeprocessedaswellasstored.Itisthusnecessarytoautomatetheprocessingandtoreducedatabycalculatingestimatesthatarerelevanttoandaccessiblefortheendusers.
Processingofdataisnotuniquelydefined.Thereareanumberofoptionalmethodsthatcanbeemployed.Dependingonthemethodthestatisticalestimatemaychange,however.Theresulting
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valuewillbeinfluencedbyforexamplethewindowsizeusedinestimatingthespectrumortheaveragingtimelength.Theaimofthestandardpresentedhereistomakedatafromdifferentstudiesandanalysescomparableandcompatible.
Itistheenduserthatdefinestheprecisionneededinestimates,what‘relevantestimates’implyandtowhomthepresentationofdataisaimingat.InthecaseoftheMSFDdescriptor11therelevantestimatesaretheannualaveragesofthe1/3-octavebands63and125Hz.BIASintendstoalsoestimateanumberofadditionalparametersbasedonthemeasureddata.
E. Standards on signal processing
I . Quality control of data Qualityassurance(QA)referstotheimplementationofsystematicactivitiessothattherequirementsensuringreproducibleresultsarefulfilled.Forqualityassurance,theanalysisistobedoneinasystematicway,followingguidelinesandstandards.Itentailsmonitoringofprocessesandfeedbackloopswhichconfererrorpreventionandinspectionprocesses.Oneexampleforafeedbackloopisthering-test.Thering-testisaqualityassurancetoolfortheanalysisroutinesofdifferentorganisationsthatshouldreturnsimilarresultstoensurethatthecorrectpracticeindataanalysisisappliedandresultsreproducible.Thestatisticalevaluationandinterpretationoftheco-operators’resultsisanimportanttoolforassessingthequalityandcomparabilityofallparticipants’analyses.Toestablishandensuretherequireddataqualityitisessentialtounderstandanddefinethepurposeoftheconductedstudy.
Conductingsignalprocessingtohighstandardsrequirestheidentificationofpossibleerrorsources,i.e.identifyingwhichprocessesdeliveruncertainresults.Identifyingthoseallowthedefinitionofanuncertaintybudget(alistofpossibleerrors)connectedtosignalprocessing.Signalprocessingishoweveronlyonesourceofpossibleerrors.Onehastobearinmindthattherearemoreerrorsourcessuchasthemeasurand,instrumentationmetrologicalperformance,calibration,sampling,interface,userandenvironmentalconditions.
Assessinguncertaintiesinsignalprocessingisnecessarytoimprovethequalityofthemeasurements.Forgivingameasureofconfidencetoacalculatedvalue,possiblecalculationerrorsshouldbedeterminedandthelikelysignificanceoftheeffectontheresultidentified.Theuncertaintiesconnectedtotheresultsofthesignalprocessinganalysiscanbeexpressedasanintervalinwhichtheerrorvalueswillliewithahighprobability.Theprocessofsystematicallyquantifyingerrorestimatesisknownasuncertaintyanalysis.Therearetwogeneralclassesofuncertainty:
• Randomuncertainty,whichcanbeassessedbyrepeatingcalculationsandexaminingthestatisticalspreadintheresults.Indigitalsignalprocessingitispossibletomakerepeatedcalculationsbyusingthesamerawdata.Randomuncertaintyisameasureoftheprecisioninthecalculations.Highprecisionisobtainedwhenthecalculationsarerepeatableandwithlittledispersioninresults,
• Systematicuncertainty,whichrepresentsapotentialsystematicbiasinananalysis,forexamplecausedbyincorrectformulaforthecalculationorprogrammingerror.Thiscategory
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ofuncertaintycannotbeassessedusingrepeatedcalculations,andmustbeevaluatedbyconsideringthepotentiallyinfluencingfactorsonthecalculationaccuracy.
Makingsurethatworkingroutinesareataproperlevelandincludepersonaltrainingandqualityassurancetoolsminimizesuncertainties.
Recommendedqualityassuranceproceduresandtoolsarethefollowing
• Controlofcomputersystem• Testingofsignalprocessingsoftware• Guidelinesandstandards• Monitoringofprocesses• Feedbackloops
a. Control of computer system OneQAprocedureistonominateonepersonthatisresponsibleforauthorizinganysoftwaretobeused.Withthehelpofasampledatasetitcanbedeterminedifvaluescalculatedbythesoftwarearecorrect.Thecontrollingsystemforsoftwareshouldeffectiveandauditable:eachcomputershouldhavealogwithinstalledhardwareandsoftware.Anynewsoftwareormodificationstoexistingsoftware,includingnewreleasesofcommercialpackages,hastoberecordedinthelogwithfirstdateofuse.Itisthenpossibletodeterminewhichversionofthesoftwarewasinuseatanyparticulartimeshouldanerrorneedtobetraced.
Itshouldbeassuredthat:
• Onehascontrolofwhatsoftwareisloadedontotheanalysiscomputer,• Allcomputersystems,includingsoftware,havebeencheckedtoensurethattheyrecordand
processdatacorrectly,• Allsoftwareissecuredagainstunauthorizedchanges,• Alogisusedcapturingsoftwareupdatesandsoftwarechecksonthecorrectfunctioning
afterupdate,• Aprocedureisinplaceforaregularbackupofdatastoredonthecomputers.
b. Testing of a signal processing software SoftwaretestingisaQAprocedureandinvestigationconductedtoprovideuserswithinformationaboutthequalityoftheproduct.Thetestingprocedureconsistsoftestspecification,testexecutionandtestreporting.
Whendecidingonsignalprocessingsoftwareitshouldbetestedifitfitsforonepurpose,andthatitisreliable,efficientandmaintainable.Itshouldbeassuredthat:
• Itmeetstherequirementsthatguideditsdesignanddevelopment,• Itrespondscorrectlytoexpectedinputs,• Itperformsitsfunctionswithinanacceptabletime,• Itachievestheresultitsusersdesireanditcanbeinstalledandruninitsintended
environments.
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InBIAS,Matlabscriptswerecompiledspecificallyforthepre-processingandprocessingofBIAS-data,whichcouldbeusedbyeachbeneficiary,althoughtheuseofthescriptswasnotobligatory(seering-test).
c. Guidelines and standards GuidelinesandstandardsareQAtoolsensuringthatproceduresaredoneinacomparablewaywithinaprojectteam.
InBIAS,aworkinggroupwasestablishedforthedevelopmentofthesignalprocessingstandardsandanyguidelinesgiventherein,discussingandagreeinguponwhichstandardstouseforthedigitalsignalprocessingofBIAS.Theresultsarecompiledandpresentedinthecurrentdocument.
d. Monitoring of processes Processesneedtobemonitoredforenablingtheirtraceabilityfromtherawdatatotheresultsandviceversa.InBIAS,instructionsandprotocolswerepreparedtailoredtotheBIAS-needstoguarantytheconductionofproceduresinastandardisedwaybyallbeneficiaries.
e. Feedback loops Feedbackloopsenablethecrosscheckingofroutinesforatimelydetectionoferrors.OnefeedbackloopusedinBIASisaring-test,forwhichidenticalsoundsamples,providedbytheQAcoordinator,wereanalysedbyallbeneficiaries.ThesampledatapackageincludedsignalsamplesinWAVformat,totalhydrophonesensitivityorfull-scalevalueandsetgain.Additionally,software(Matlabscript)foranalysiswasalsoincluded.EachBIASbeneficiarycarriedoutthedataanalysisonthesampledatausingthemethodandsoftwareasusedfortheanalysisoftheBIASdataobtainedatthemeasuringstationstosecurethattheresultsarecomparable.TheycouldchoosetoeitherusethecommonMatlabscriptdevelopedfortheBIASanalysisortousetheirin-housesoftware.Itisimportantthatthesamemethodandsoftwareisusedinthering-testthatlaterwillbeusedfortheBIASrawdataanalysis.IfothersoftwarethanthecommonMatlabscriptwasusedthenithadtobeassuredthattheheadingformatwasadaptedtothein-housesoftwareasrequired.
Errorsthatcanbeidentifiedwitharingtestare:
• Applicationofwronghydrophonesensitivityand/orinstrumentgainfactors,• Choosingthewrongsignalchannelwhenmorethanonechannelwasused,• Different1/3-octavefilterconfigurations,• Individualorunclearconfigurationofthefileheading,• Differencesbetweensoftware,softwarescriptsorsoftwareversionsused(theseweresmall
inBIAS,lessthan1dB).
I I . Pre-processing Pre-processingisabasicstepinsignalprocessingtopreparedatafordataanalysisandtoassessthequalityofthedata.
Thepre-processingprocedureinBIASconsistsofanumberofconsecutivestepsasfollows:
• Measuringself-noise,• Organisationofrecordeddata,
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• Testingofdatacoverage,• Testingoffilesizeandfilelengthoftherecordedfiles,• Testingofnon-numericalvaluesintherecordeddatafiles,• Testingofclipping.
a. Measuring self -noise Itisimportanttoreassurethattheself-noiseofeachrecordingsystem(includingtheanchoringsystem)doesnotexceedtheambientnoiselevelsprevailingatthemeasuringstation.Thisshouldbedonebymeasuringandcomparingtheself-noisetotherecordednoiselevels.
b. Organisation of recorded data Foreachstation,theamountofdatahastobedetermined,i.e.thenumberoffilespermonthandstation,andthecorrespondingfilesizes.Alldatafilesforonestationandaspecificmonthareplacedinacorrespondingdatafolder.Datarecordedbeforedeploymentafterretrievalofthesystemaretoberemoved.Datarecordedduringdeploymentandretrieval,whilethedeploymentshipwasclosetothesensorandcontributedtotherecordedambientnoise,arealsotoberemoved.
c. Testing of data coverage Forcalculatingthedatacoverage,thetimeanddurationofrecordeddataarecomparedrelativetotheplannedrecordingtimeandduration.Thecoverageisgivenaspercentageofperiodcoveredperperiodplanned.
d. Testing of f i le size and f i le length Pre-processingshouldentailatestofthefilelengths.IntheBIASprojectaminimumof15minutesperhourrecordingsarerequired.TheMatlabscriptbias_gaps.m(seeSectionG.I.d)notesthetimedifferencebetweentherecordinglengthandtherequiredminimumtimeof15minutes.Thisscriptteststhetimelengthandthesizeoftherecordedfilesbyreadingtheheaderofthefiles.Thetestofthefilelengthisdonetoassurethatallfileshavethesamelength.
e. Testing of non-numerical values Fileswithcorrectlyrecordeddatacontainonlynumericaldata.Inthescriptbias_mean.m(SectionG.I.a)thequalityofthedatasamplesarecheckedfornon-numericalvaluesNaN(not-a-number)andInf(toohighvalueforanumericalrepresentation).NaNistheIEEEarithmeticrepresentationforNot-a-Number.ANaNisobtainedasaresultofnotdefinedoperationssuchas0.0/0.0.InfreturnstheIEEEarithmeticrepresentationforpositiveinfinity.Infinityisalsoproducedbyoperationssuchasdivisionsbyzero,(e.g.1.0/0.0).IfNaNandInfareobtainedinthedatathesevaluesareomittedpriortoprocessing.ThepresenceofNaNorInfintherawdatacanleadtoerrors,whicharedifficulttotrace,asthebehaviourofprocessingfunctionswhenencounteringNaNorInfcannotalwaysbepredicted.
f . Testing of cl ipping Whenreceivedsignalsareofhighersoundpressurethanwhatcanbepickedupbytherecordingsystem,thesystemgetssaturated.Thecorrespondingdigitisedvaluewillreturnthemaximumsoundpressurethatthesystemisabletorecorded,whichislowerthanthetruevalueofthesignal.Thisphenomenoniscalledclipping.Fordeterminingthequalityofdata,theamountofpositiveandnegativeclippingistobequantified.Theamountofclippingiscalculatedandpresentedaspercentageofclippeddatasamplesforaperiodof20s.Therecommendationistoflagforclipping
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whenatleast0.1%ofthesamplespersecondofdataisclipped.Clippingoflessthan0.1%ofthesampleswillaffecttheestimatesoftheanalysisinsignificantly.Informationontheflaggeddatashouldbenotedintheprocessingprotocolforfurtherconsiderationandtracking.Anyfurtherproceduresdecideduponwithregardstotheflaggeddatashouldbetrackedcarefully.
TheMatlabscriptsdesignedfortheBIASpre-processing(SectionK.I)areusedondatarecordedwithLoggerheadandWildlifeautonomoushydrophonesensors.
I I I . Processing ProcessingofdataconstitutesthemainanalysisoftherawdatawiththeaimofextractingthefundamentalmeasuresneededformodellingsoundpropagationandtosumupthestatisticsforthenationalresponsibleagenciesandtheirreportstotheEuropeanCommission.TheCommissionDecision2010prescribestheEuropeanmemberstatestomonitorunderwaternoiseinthe1/3-octavebandsaround63and125Hz(CommissionDecision2010/477/EU,2010).
1/3-octavebandanalysisisawell-establishedmethodtofilterasignalwithabankofoverlappingfilters,allwiththesameQratio,whichistheratiobetweenbandwidthandcentrefrequency.Thespecificationsforsuchfilterbanksarewellestablishedininternationalstandards(IEC1995(EN61269),1996andANSI,2004).Thereareseveralreasonsforchoosinga1/3-octavebandanalysis;mostimportantly,therearephysiologicaljustifications:Substantialexperimentalevidencefromhumans(Scharf,1970)andothermammals(Fay,1988)showthatthemammalianearcanbewellmodelledbyafilterbankconsistingofoverlappingfilters,roughly1/3-octavewide.Thefilterbandwidthisknownasthecriticalbandwidthandcanbemeasuredexperimentallybymaskingexperiments.Thedetectionthresholdofapuretoneismeasuredwhileapplyingnoisewithincreasingbandwidtharoundthetone.Whenthebandwidthofthenoiseislargerthanthecriticalbandwidth,thedetectionthresholdofthetoneisconstant,i.e.independentofthenoisebandwidth.Whenthebandwidthofthenoiseissmallerthanthecriticalbandwidth,thedetectionthresholdofthetonedecreaseswithdecreasingnoisebandwidth.Thecriticalbandwidthisthusdeterminedasthebandwidthwherethethresholdstartstodecrease.Asimpler,butindirectestimateofthefilterbandwidthisaccomplishedthroughmeasuringtheso-calledcriticalratio(Fletcher,1940).Thecriticalratioistheratiobetweentheintensityofapuretonesignalandthespectrumdensitylevelofabroadbandnoisemasker(whichhasaflatfrequencyspectrum)atthedetectionthresholdofthemaskedtone.BecausethenoiseisgiveninunitsofintensityperHz,thecriticalratiowillautomaticallybegiveninunitsofafrequency.Asubstantialnumberofstudieshavebeenconductedwherecriticalbandand/orcriticalratioweremeasuredindifferentmarinemammals.Thesestudiesincludebottlenosedolphin(AuandMoore,1990),harbourporpoise(Kasteleinetal.,2009)andharbourseal(Southalletal.,2003)andaregenerallyconsistentwithaconstant-Qfilterbankmodel,althoughwithconsiderablevariation.Inactualmeasurementsthebandwidthsderivedfromcriticalratiomeasurementsareusuallyconsiderablysmallerthanthosederivedfromdirectmeasurementsofthecriticalband.ThecriticalbandwidthandthecriticalratiovaluesarenotthesamebecausecentralassumptionsmadebyFletcher(1940)inderivingthecriticalratioarenotmetbythemammalianear.
Thusa1/3-octavefilterbankappearstobeausefulfirstapproximationformodellingtheabilityofmarinemammalstodetectnarrowbandsignalsinnoise.
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1/3-octavefiltershavetraditionallybeenmeasuredusinganaloguefiltersbuthavenowbeenalmostentirelyreplacedbydigitalsignalprocessingequivalents.Themostdirectwayistouseasetofdigitalband-passfilters,butsuchfilteringcanbeslowanddifficulttoimplement,andgreatcarehastobetakentomakesurethefiltersarestable.Themainproblemwithadigitalfilterbankisthataverylargenumberoffiltercoefficientsisrequiredtocoverthelowfrequencies,oralternatively,asdoneinthefiltbankfunctionforMatlab,recursivelydown-samplethesignal.AmuchfasterandsimplerapproachistousefiltersbasedonaFastFourierTransformation(FFT).ThefiltersareconstructedtoconformascloseaspossibletotheIEC/ANSIstandardforthistypeoffilters.
Centre,loweranduppercut-offfrequenciesofthestandard(IEC/ANSI)filtersaregivenas:
𝑓! 𝑥 = 𝑓!𝐺 !!!" !
𝑓!"# 𝑥 = 𝑓! 𝑥 𝐺!! !
𝑓!!"! 𝑥 = 𝑓! 𝑥 𝐺! !
where
fmisthecentrefrequency
flowisthelowercut-offfrequency
fhighistheuppercut-offfrequency
xistheIEC/ISObandnumber,
fristhereferencefrequency(1,000Hz)
Gistheoctaveratio(base10system)with𝐺 = 10! !"
Anothercommonstatisticalquantitywidelyusedinnoiseengineeringisthepercentilelevel.ItisdefinedasthelevelLNthatisexceededforNpercentofthetimeintervalconsidered.Forexample,L1isthelevelthatisexceeded1%ofthetime.TheL1canbeusedasameasureforthemaximumlevel.Itisamorerobustestimatethanthesinglemaximuminthedata,sincetheabsolutemaximumispronetoheavybiasfromsingularevents,suchasrattlingoftheanchoringsystemorelectrictransientnoise.Accordingly,L99andL95areusedtodescribetheminimumlevel.L50isthemedianlevel.
ThefundamentalrequirementoftheBIAS-processingistoprovidemeansoundpressurelevelsinthe1/3-octavebandscentredon63Hzand125HzasrequiredbytheMSFD.ThefinalversionoftheReportoftheTechnicalSubgrouponUnderwaterNoiseandotherformsofenergy(VanderGraafetal.,2012)pointsoutthatambientnoisepeaksathigherfrequencylevelsthanthesetwofrequenciesbands,especiallyinshallowwater,suchastheBalticSea.TheTechnicalSubgroup(TSGNoise)thereforesuggeststhathigherfrequencybandsshouldalsobemeasuredandanalysed,astheymayprovetobevaluableinfuture.TheBIASteamsharesthisviewanddecidedtoadditionallyprocessthe2,000Hz1/3-octaveband.Furthermore,thebroadbandsoundpressurelevelsaredetermined.
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Processingofthesounddataincludes:
• Filteringdata,• Analysingdata.
Processingshouldbeperformedonlyonquality-controlledandpre-processeddata,sothattheoutputhassufficientqualityforanymodellingandreporting.Inreality,however,theremaybeaneedforadditionalqualitycontroloftheoutputtorevealirregularitiesnotcapturedbythepre-processing.
a. Filtering data Theextractionoftheenergyinthethree1/3-octavebandsismadewithanFFT-filter.Thisfiltercalculatesthesumoftheenergyfromfrequencybinscoveredbythefrequencyrangeofthe1/3-octaveband.ThespectrumisderivedbycalculatingtheDiscreteFourierTransform(DFT)ofthesignal.ThefiltersusedinBIASarebandsnumber18,21and33.Bandnumber18and21arethe1/3-octavebandscentredat63Hzand125Hz,respectively,asrequiredbytheMSFD,whilenumber33iscentredat2000Hz(Table1).SeveraladjacentFFT-binscanbesummedtoapproximatea1/3-octavebandfilter.Thebandwidthwillhowevernotmatchexactly,asthereisafinitenumberofbinsperFFT-filter.Anadjustmenttermisaddedtocorrectforthis.
𝐵𝑊!"##$!%&"' = −10 log!"!!!"#$!%!!!"!!!!"#
,
where
BWcorrectionisthecorrectionfactortoaddtothesumacrossbins,
BWactualistheactualbandwidthvalue.
1/3-octavefiltersrealisedasFFT-filtersareverysteep,muchsteeperthantraditionalhardwarefilters,butstillwithintheIEC/ANSIspecifications.Filtersathigherfrequenciesfitbettertothestandardwhereasthediscrepancybecomessubstantialatthelowestfrequencies.
Withregardstothefiltering,theBIASrecommendationistoderivetheDFTwithanominalbandwidthof1Hz.ThisisacompromisebetweentimeresolutionandthepossibilityofimplementingFFT-basedband-passfilterswithreasonableshapeevenatlowfrequencies.Usingasegmentsizeof1sfortheDFT,thenominalbandwidthoftheDFT-binsbecomes1Hz.
Table1Informationonthe1/3-octavebandfiltersusedinBIAS.FlowandisFhigharethelowerandhighercutofffrequencies,respectively.Fmisthecentrefrequency.‘Bandwidthcorrection’isexplainedinthetext.
IEC/ANSI
bandno.
Nameofband Flow
(Hz)
fm
(Hz)
Fhigh
(Hz)
Numberof1Hzbands
Bandwidthcorrection(dB)
18 63 56.2 63.1 70.8 14 0.17
21 125 112.2 125.9 141.3 29 0.1
33 2000 1778 1995 2239 460 0.01
BIAS SIGNAL PROCESSING
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b. Analysing data TheBIAScalculationoftheaverageisdoneinanumberofconsecutivesteps.Thebasisforthesestepsisthepre-processeddataofrecordedtimeseriesfromeachBIAS-beneficiary,withdifferentsamplingratesdependingontypeofsensorusedbythebeneficiary.Inthefirststep,anFFT-analysisisdoneoverconsecutive1-secondperiods,givingamplitudespectrawitha1Hzresolution.Inthesecondstep,thesoundpressurelevels(SPL)asdefinedinVerfussetal.(2014,2015)arecalculatedintherequired1/3-octavebandsover1second.The1-saveragesarethenfurtherprocessedtoaveragesover20seconds.
Estimatessuchashourly,dailyandmonthlyaveragesarecalculatedbasedonthe20-saverages,eventhoughthesearenotrequiredbytheMSFDdescriptor11.Finally,theannualmeanisalsoderivedusingthe20-saverages.InBIASmonthlyandannualarithmeticmeansandthepercentilelevelsareestablishedfor63,125and2,000Hzaswellasforthefrequencybandfrom10to10,000Hz.
ForspatialmodellingtheBIAS-recommendationistousethe20-secondaverageddata.Thisisacompromisebetweenasufficientlylongtimeintervaltoobtaingoodestimatesofthemeanandanintervalshortenoughforthenoiselevelfromnearbyshipstoremainapproximatelyconstantwithineachsegment.
IV. Presentation of results Thissectionshowsexamplesforcommonorusefulrepresentationsofambientnoisedataforastandardisedanduniformpresentationofnoisemeasurementresults.Formattingaspectsofgraphicalrepresentationofdata,suchaschoosingameaningfulaxisrange,captionsize,whetherornottoincludeagrid,orwheretoplacethelegend,willnotbegivenhere.
ForthedataobtainedintheBIASprojecttheBIASbeneficiaryagreedonthefollowinggraphicalpresentationsofthenoisemeasurements:
• Levelversustime,• 1/3-octavespectrum,• Levelstatistics:levelhistogram,cumulativedistribution,percentilelevels.
Thegraphsarebasedonsoundpressurelevelsthatwerecomputedinstepsof1second.
Theexamplesshownhere,inparticularthosefromtheBIASposition31(Fehmarnbelt)showratherhighsoundpressurelevelvaluescomparedtomeasurementsfromotherplacesintheBaltic.ThisreflectsthehighambientnoiselevelscausedbyaveryhighshippingdensityinthiswesternBalticarea.
a. Level versus t ime InLevelversustimeplots,thesoundpressurelevel(SPL;definedinVerfussetal.,2014,2015)isusuallypresentedinaclassical"stripchart"diagram.Thesimplestoptionforalevelversustimeplotistoplotthe1-secondlevels,i.e.anSPLwithanintegrationtimeover1second,theoutcomeofthefirstanalysisstepwithoutfurtherprocessing.Anexampleforthe1/3-octavelevelsforthe63Hzand125HzbandsaswellasthebroadbandleveloverthewholerecordingbandwidthisgiveninFigure1.
BIAS SIGNAL PROCESSING
17
Thebroadbandlevelisusefulforaplausibilitycheckofthedata,asthe63Hzand125Hzlevelscanneverexceedtheoveralllevel.
Figure2showsthe20-second-averagesoundpressurelevelsofthedatashowninFigure1,meaningthattheyareslightlymoreprocessed.
InBIASitwasdecidedthatthetimescaleshouldrefertothestartofanaveraginginterval,whichwasimplementedinFigure2:Adatapointate.g.13:05representsanaveragingintervalfrom13:05:00to13:05:59.
Figure1.TwentyminutesrecordingfromtheBIASstation31,Fehmarnbeltwithashippassingbyverycloselyat13:04.Thecurvesshow1-secondsoundpressurelevelsofthefirstanalysisstepwithoutfurtherprocessing.
Figure2.20-second-averagelevels(20secondsLeq)computedfromthedatashowninFigure1.
BIAS SIGNAL PROCESSING
18
b. 1/3-octave spectrum Amorecomprehensivedescriptionofthenoisemeasurementsrequiresthecalculationofthefrequencyspectrum.Forunderwaterambientnoise1/3-octavebandsisusuallyanadequatefrequencyresolution.Analternativespectralrepresentation,whichwillnotbefurtherdiscussedinthecurrentdocument,isthenarrowbandspectrum(usuallyin1Hzintervals).Suchapresentationismoresuitablewhentonalfeaturesofasoundsignalshallbeanalysedindetail,e.g.inacousticshipsignaturesorwindturbineoperatingnoise.
Examplesof1/3-octavespectraareshowninFigure3.Themaximumfrequencythatcanbedisplayedinaspectrumdependsonthefrequencyresponseoftherecordingsystemandthesamplingfrequencyused.FortheWildlifeAcousticsSM2Mwithasamplingfrequencyof32kHz,forexample,thehighestusable1/3-octavebandis12.5kHz.
Figure3.1/3-octavespectrum(60secondsLeq)fortwopointsintimefromFigure2:Trianglesshowthecloseapproachofashipat13:04,andopencirclesshowthespectrumtenminuteslater.
c. Level histogram and cumulative distr ibution Amethodtoobtainstatisticalinformationfromnoiserecordingsistoplotahistogram,adistributionofthebasic1-secondlevels(Figure4).Iftwoormorehistogramsshallbedisplayedinthesamediagram,straightlines(Figure5)maybemorecomprehensiblethantheclassicalbargraphstyle.Inbothfigures,thevaluesarenormalized.
Attimes,acumulativedistributionisshowninsteadofahistogram(Figure6),representingthepercentageofmeasurementvaluesthatarelowerorequaltothevaluegivenonthey-axis.However,littleadditionalinformationcanbeobtainedfromitcomparedtothelevelhistogram.Featuressuchastheskewnessorasecondarymaximum,whichmaywellbeseeninahistogram,aredifficulttorecognizeinacumulativedepiction.
BIAS SIGNAL PROCESSING
19
d. Percenti le level Percentilelevelscanbecombinedwithalevelversustimediagramandalsowithaspectrumdisplay.PossiblerepresentationsareshowninFigure7andFigure8.Asbefore,thegraphsarebasedon1-secondaveragesofthesoundpressurelevel.
Figure4.Levelhistogramsof1secondvaluesfor63Hz1/3-octavebandandfortheoveralllevel,computedfromthreemonthsofrecording(dutycycleis20minutesofrecordingperhour).
Figure5.AlinegraphismorecomprehensiblethanthebargraphstyleofFigure4.,iftwoormoredistributionsaretobeshowninthesamediagram.
BIAS SIGNAL PROCESSING
20
Figure6.100-CumulativepercentageofthedatashowninFigure1.
Figure7.Dailypercentilelevels(i.e.intervallength=86400s)ofthe125Hz1/3-octavebandatposition31,Fehmarnbelt,forJanuarythroughMarch2014.
BIAS SIGNAL PROCESSING
21
Figure8.Percentilelevelsatposition31,Fehmarnbelt,forJanuarythroughMarch2014.Thestatisticalanalysisisbasedon2.592·106values(0.333·86,400secondsperday·90days),sincetheprimarydataintervalis1sandtherecordingdutycyclewas20m/60m=0.333.
V. Data Management Thequalityofthedatamanagementisofhighimportancesinceitwilldirectlyinfluencethemanagementoftheresultsandinparticularthemodellingtasks.
Acousticdata,onceprocessed,needstobesharedinordertoprovideacommondatasettobeusedformodelling.Sincethemodellingismostlydoneataregionalscale,thedatamanagementruleswillensureuniformityanduniversalityoftherepresentationandthestorage.Aspecialcareshouldbetakentothefollowings:
• Theoverallvolumeofacousticdatacanrapidlybeveryhigh.Itisrecommendedtohandledatainchunksnotlongerthanone-monthperiods;
• Non-acousticparameterssuchasinstrumentgain,samplefrequencyorhydrophonedepth,shouldbereportedwithamaximumofcare.Automaticnumericaltranscriptiontoolsshouldbedevelopedsince:
o Themanualtranscriptionoftheseparametersfrompaper-formsfiledat-seaisapotentialsourceoferrors;
o Themanualtranscriptionoftheseparametersintheprocessingtoolsisapotentialsourceoferrors.
• Thequalitycheckoftheprocesseddatashouldbedoneusingallsortsofdataofopportunity.Forinstance,thedepthofthehydrophoneshouldbecomparedtothebathymetricmaptoverifytheconsistencyofthevaluesreported.
ForBIAS,thefollowingcommonguidelinesandformatsaredefinedwhichwillensureaccessandhandlingoftheprocesseddatainauniformedanduniversalmanner.Therefore,thissectionaddresses:
BIAS SIGNAL PROCESSING
22
• Theproceduretodeliverthefilesinadatasharingplatform;• Thecommonconventionstobeadopted;• Theformatofthefilesthatwillcontenttheprocesseddatatobeshared;• Thecontentofthefiles.
a. Delivery procedure Thedeliveryprocedure(Figure9)isspecifiedformaximizingthelevelofefficiencywhenmultipleorganizationexchange,processandexploittheprocessedacousticfiles.Theprocedurecanbeimplementedforanyperiodoftime.IntheBIASproject,ithasbeendecidedthateachperiodisequaltoacalendarmonth.Theprocedureincludesaqualityassessmentphasewhichobjectiveistoinsuretheconsistencyofthedatasetandtoavoiderrorsbeforethedatasetismadeavailable.
Twolevelsofresponsibilityhavebeendefined(Figure9):
• Thebeneficiarylevelcorrespondstoorganizationsthatareresponsibleforimplementingthemeasurementsandthesignalprocessing.Beneficiariesareresponsibleforarchivingthedataissuedfromtheirmeasurementstations,andtosplitthedatasetintogroupsforeachperiodoftime(inBIAS,eachcalendarmonth).Theprocessingoftherawdataisperformedforeachperiodseparately.Headerinformationareupdatedaccordinglytoinsurethatmeta-dataassociatedtoeachdatasetiscorrect.Somequalitycheckshouldbeperformedinordertoavoidanomaliesintheprocessedfilesproduces.
• Thequalitymanagerlevelcorrespondstoauniqueorganizationthatisresponsibleforqualitycheckofalltheprocesseddata.Thequalitymanagerwillgatherthedatafromeachcontributorsorbeneficiaries,andwillperformthefollowingqualityverificationsinordertoensure:
o Thatdatahavebeenprocessedforeachmeasurementstation;o ThatthefilenamesarecompliantwithspecificationdefinedinsectionE.V.c;o Thattheprocesseddataisconsistentwiththedeploymentperiodsforeach
measurementstation;o Thatallthedataarepresentforthegivenperiod;o Thatallexpected1/3-octavebandshavebeenprocessed;o Thatallfilesarenotemptyorsuspiciouslysmall;o Random1validationofmeasurementstationpositions;o Randomcheckoftheheader;o Randomcheckoftheformat.
1Randomvalidationshouldbedoneonaminimumof5filespermeasurementperiod.
BIAS SIGNAL PROCESSING
23
Figure9.Datamanagementprocedureoftheacousticfiles.
b. Common Conventions Theprocessedacousticdatasetaremadeoftheacousticdataitself,butalsoofdescriptivedatasuchastimeofmeasurementandposition.Thosedatashallbereleasedaccordingtothefollowingconvention.
i. Acoustic quantity to be delivered for the modelling
Theacousticquantitytobedeliveredforthemodellingis20-secondaveragesoundpressurelevels.
ii. Time convention
AlltimeindicationsshouldusetheUTCformat.
iii. Position convention
Alllatitudeandlongitudearetobeexpressedindecimaldegrees.PositivelatitudesaretowardsNorth.PositivelongitudesaretowardsEast.
c. Format Thissectiondescribestheformatofthefilescontainingtheprocessedacousticdata.FilesaretobegivenasASCIIfiles.ThestandardC-languageconventionisusedtodescribetheformatfornumbers,asillustratedinTable2.
BIAS SIGNAL PROCESSING
24
Table2.FormatsofnumbersinstandardC-languageconvention.
Format Explanation Example
%03dIntegercodedwith3digits.Ifthevalueislessthan100,forcethenumbertobepaddedwith0s.
074
%05dIntegercodedwith5digits.Ifthevalueislessthan10000,forcethenumbertobepaddedwith0s.
01984
%8.2fFloatingpointwrittenwithmaximum8charactersandwith2decimalsdigitsprintedafterthedecimalpoint.
534.24
%11.8fFloatingpointwrittenwithmaximum11charactersandwith8decimalsdigitsprintedafterthedecimalpoint.
98.98747263
%5.1fFloatingpointwrittenwithmaximum5charactersandwith1decimaldigitprintedafterthedecimalpoint.
234.6
%10.8fFloatingpointwrittenwithmaximum10charactersandwith8decimalsdigitsprintedafterthedecimalpoint.
9.98747263
i. Content of the processed acoustic files
Eachfilehastocontainthevaluesofonlyonesinglebandwidthoftheprocessedacousticdata.Theamountofdatacontainedineachfileisnotlimited.Itishoweverrecommendedtorestrictthefilelengthtoonemonthofdata.
ii. Names of the processed acoustic files
Eachfileistobenamedusingthefollowingconvention:
StationIdSSS_BBBFFFHz_StartYYYYMMDDHHMMSS_EndYYYYMMDDHHMMSS.asci
Where:
• SSSistheStationId,format:%03d;• BBBisakeyworddescribingthebandwidth.Itshallbe‘ThirdOctave’or‘BroadBand’
accordingtothenatureoftheacousticdatathatisstoredinthefile;• FFFistheroundedcentralfrequencyofthebandwidthwhenBBBisnot‘BroadBand’;format:
%05d;• Startdesignatethetimeofthefirstacousticdatacontainedinthefile;• Enddesignthetimeofthelastacousticdatacontainedinthefile;• YYYYMMDDHHMMSS:Year,Month,Day,Hour,Minute,Second
d. Content of the processed acoustic f i les Eachfileismadeofaheaderandatable.Theheaderofthefilegivesinformationaboutthemeasurement.Theheaderwillallowtheuserofthefiletogetthecontextofthedatathatispresentinthetableofthefile.Thefollowingsectiondescribeshowtoformattheheaderandthetable.
BIAS SIGNAL PROCESSING
25
i. Header
Theheaderofthefile
Organizationname %Issuedby StringStationId %StationId Integer%03dStationName %NameoftheStation StringLoggerId %DataLoggerunitId StringDataType %Typeofacousticdata ‘SPL’or‘SEL’dBunit %Unitsoftheacousticdata StringBandType %ThirdOctave/Broadband StringFc %Centralfrequencyoftheband Float %8.2fFmin %Minimumfrequencyoftheband Float %8.2fFmax %Maximumfrequencyoftheband Float %8.2fWindow %Processingwindow(sec) IntegerLatitude %LatSigneddecimaldegrees,WGS84 Float %11.8fLongitude %LonSignedDecimaldegrees,WGS84 Float %11.8fL %Heightabovebottom(m) Float %5.1fH %Waterdepth(m) Float %5.1fHdate %Dateofwaterdepthmeasurement(UTC) StringYYYYMMDDIssued %Dateofissue StringYYYYMMDDProgramName %Processingprogramused StringSync %SynchronisationDateofDataLogger StringYYYYMMDDDrift %LoggerDrift(s/day) Float %10.8fN %Numberoflinesinthetable Integer
ii. Table
Eachrowhastoincludecolumnswiththefollowingformat:
• 1stcolumn:Date–String-YYYYMMDDHHMMSS• 2ndcolumn:MinimumLevel(%6.2f):forM-secondvalues,theminimumlevelisdefinedas
theminimumoutoftheMvaluesof1-secondprocesseddata,withM=Window(integer);• 3rdcolumn:MeanLevel(%6.2f):forM-secondvalues,themeanlevelisdefinedasthe
geometricmeanoftheMvaluesof1-secondprocesseddata;• 4thcolumn:MaximumLevel(%6.2f):forM-secondvalues,themaximumlevelisdefinedas
themaximumoutoftheMvaluesof1-secondprocesseddata;• 5thcolumn:Standarddeviation(%6.2f):forM-secondvalues,thestandarddeviationis
definedasthestandarddeviationoftheNvaluesof1-secondprocesseddata;• 6thcolumn:Ratioofpositiveclippingsamplesintheprocessedwindowtothenumberof
samplesintheprocessedwindow(%10.8f)–Numberbetween0and1.• 7thcolumn:RatioofNegativeclippingsamplesintheprocessedwindowtothenumberof
samplesintheprocessedwindow(%10.8f)–Numberbetween0and1.
EachcolumnisseparatedbyaTAB.
iii. Case of no acoustic data for a station Inthecasethatastation/recorderhasnorecordeddataornovaliddataafterprocessing,foranyreason,alltheprocessedfilesmuststillbeproducedandthefilenamemustagreewithformat
BIAS SIGNAL PROCESSING
26
describedinSectionc.ii.Incaseofno-data,thefilemustcontainonlythetextnodatainthefirstline,noothertext.
iv. Case of one value is unknown in the header
Inthecaseofaheadervalueisunknownthefieldmustbereplacedbyadash.
e. File template
i. File name StationId031_ThirdOctave00063Hz_Start20140101000000_End20140104211240.asc
ii. File content
FOI %Issuedby031 %StationIdTourEiffel %NameoftheStationSM2M-08 %DataLoggerunitIdSPL %TypeofacousticdatadBref.1µPa: %UnitsoftheacousticdataThirdOctave %ThirdOctave/Broadband63.00 %Centralfrequencyoftheband56.23 %Minimalfrequencyoftheband70.79 %Maximalfrequencyoftheband20 %Processingwindow(sec)48.858093 %LatitudedecimaldegreeWGS84-2.294694-2.294694 %LongitudedecimaldegreeWGS843.63.6 %Heightabovebottom(m)53.4 %Waterdepth(m)20131213121355 %Dateofwaterdepthmeasurement(UTC)20140507 %Dateofissueleif_program(1.0).m %Processingprogramused20131214 %SyncDateofdatalogger0.205284 %Drift(s/day)7 %Numberoflinesinthetable
%TimeUTCMinMeanMaxStdPosClippingNegClipping
20140101000000 100.82 102.50 105.01 1.41 0.00000000 0.0000000020140101000020 102.21 104.33 106.98 1.33 0.00000000 0.0000000020140101000040 99.89 100.01 101.02 0.22 0.00000000 0.0000000020140101000100 98.76 100.01 101.02 0.22 0.00043000 0.0003100020140101000120 102.21 104.33 106.98 1.33 0.00000000 0.0000000020140104211220 99.89 100.01 101.02 0.22 0.00007700 0.0008900020140104211240 100.82 102.50 105.01 1.41 0.00000000 0.00000000
iii. Data storage
Thedatastoragehastobecompliantwithspecificationsmeetingthegeneralrequirementsforstoringdataonadataplatform.
BIAS SIGNAL PROCESSING
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F. References ANSI.SpecificationforOctave-BandandFractional-Octave-BandAnalogandDigitalFilters.
S1.11[ReaffirmedbyANSI2009].2004.USA,AmericanNationalStandardInstitute.
Au,W.W.L.andP.W.B.Moore.1990.CriticalratioandcriticalbandwidthfortheAtlanticbottlenosedolphin.TheJournaloftheAcousticalSocietyofAmerica88:1635-1638.
Bjørnø,L.Man-madecontributionstoambientnoiseintheseas.Alippi,A.andCannelli,G.B.ProceedingsoftheFourthEuropeanConferenceonUnderwaterAcoustics2,543-548.1998.Rome.
Bloomfield,P.1976.Fourieranalysisoftimeseries:Anintroduction.JohnWileyandSons,NewYork.
Bloomfield,P.2000.Fourieranalysisoftimeseries:Anintroduction.Secondedition.Wileyseriesinprobabilityandstatistics.JohnWileyandSons,NewYork.
CommissionDecision2010/477/EU.2010.CommissionDecisionof1September2010oncriteriaandmethodologicalstandardsongoodenvironmentalstatusofmarinewaters(notifiedunderdocumentC(2010)5956)(TextwithEEArelevance).OfficialJournaloftheEuropeanUnionL232.
Fay,R.R.1988.Hearinginvertebrates:apsychophysicsdatabook.Hill-FayAssociates.
Fernandez,A.,J.F.Edwards,F.Rodriguez,A.E.DeLosMonteros,P.Herraez,P.Castro,J.R.Jaber,V.Martin,andM.Arbelo.2005.Gasandfatembolicsyndrome involvingamassstrandingofbeakedwhales(FamilyZiphiidae)exposedtoanthropogenicsonarsignals.VeterinaryPathologyOnline42:446-457.
Fletcher,H.1940.Auditorypatterns.TheJournaloftheAcousticalSocietyofAmerica12:47-65.
IEC1995(EN61269).Electroacoustics-Octave-bandandfractional-octave-bandfilters.1996.Geneva,Switzerland,InternationalElectrotechnicalCommission.
Kastelein,R.A.,P.J.Wensveen,L.Hoek,W.W.L.Au,J.M.Terhune,andC.A.F.deJong.2009.Criticalratiosinharborporpoises(Phocoenaphocoena)fortonalsignalsbetween0.315and150kHzinrandomGaussianwhitenoise.TheJournaloftheAcousticalSocietyofAmerica126:1588-1597.
Poularikas,A.D.1999.Thehandbookofformulasandtablesforsignalprocessing.CRCPressLCC,BocaRaton.
Robinson,S.P.,P.A.Lepper,andR.A.Hazelwood.Goodpracticeguideforunderwaternoisemeasurement.2014.NationalMeasurementOffice,MarineScotland,TheCrownEstate.NPLGoodPracticeGuideNo.133.
Scharf,B.1970.Criticalbands.Pages159-203inFoundationsofmodernauditorytheory.TobiasJ.V.ed.AkademicPress,NewYork.
Southall,B.L.,R.J.Schusterman,andD.Kastak.2003.Auditorymaskinginthreepinnipeds:Aerialcriticalratiosanddirectcriticalbandwidthmeasurements.TheJournaloftheAcousticalSocietyofAmerica114:1660-1666.
VanderGraaf,A.J.,M.A.Ainslie,M.André,K.Brensing,J.Dalen,R.P.A.Dekeling,S.P.Robinson,M.L.Tasker,F.Thomsen,andS.Werner.EuropeanMarineStrategyFrameworkDirective–GoodEnvironmentalStatus(MSFDGES):ReportoftheTechnicalSubgrouponUnderwaternoiseandotherformsofEnergy.2012.
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Verfuss,U.K.,M.Andersson,T.Folegot,J.Laanearu,R.Matuschek,J.Pajala,P.Sigray,J.Tegowski,andJ.Tougaard.BIASstandardsfornoisemeasurements.Backgroundinformation,guidelinesandqualityassurance.2014.
Verfuss,U.K.,M.Andersson,T.Folegot,J.Laanearu,R.Matuschek,J.Pajala,P.Sigray,J.Tegowski,andJ.Tougaard.BIASstandardsfornoisemeasurements.Backgroundinformation,guidelinesandqualityassurance.Amendedversion.2015.
VanderGraaf,A.J.,Ainslie,M.A.,André,M.,Brensing,K.,Dalen,J.,Dekeling,R.P.A.,Robinson,S.,Tasker,M.L.,Thomsen,F.andWernerS.(2012).EuropeanMarineStrategyFrameworkDirective–GoodEnvironmentalStatus(MSFDGES):ReportoftheTechnicalSubgrouponUnderwaternoiseandotherformsofEnergy.
G. Appendix
I . Short description of Matlab scripts for BIAS
a. Bias_mean.m Thisscriptestimatesthetotalpowerin1/3-octaveband63,125,2,000Hzandinthebroadband10–10,000Hzbyintegrationinthespectraldomainforeach1secwindowandsavestheresultsinastructnameds.InputareDSGorSM2Mfiles.
b. Bias_output.m ThisscriptpreparesthedataforoutputASCIIfilesin20secarithmeticaveragesandestimatethearithmeticmean,median,standarddeviation,maxandminvaluesandthepercentageofclippinginbothnegativeandpositivedirectionineach20secblock.
c. Bias_plot.m ThisscriptmakessomeplotsofBIASvariablessuchasarithmeticmeanvs.time.AlsomakecumulativeplotsincorrespondingwithTSGgrouprecommendations.
d. Bias_gaps.m Thisscriptcanbeusedforpre-processingofDSGorSM2Mdatainwavfiles.Checkingthefilesizesandgapsbetweenconsecutivefiles.Thisscriptalsochecksgapswithinfilesforcheckingofmissingdatasamples.