Horen wat we niet meer kunnen zien
Stel je voor dat je een foto van een negentiende-eeuws marktplein bekijkt. Je ziet marktkramers, paarden met koetsen en een historische tram. Hoe zou het daar geklonken hebben? Het geklop van hoeven, de tram die davert over de sporen, het geroezemoes van stemmen… Een dergelijke scène bestaande uit verschillende geluiden, wordt ook wel een soundscape genoemd. Historici, erfgoedkundigen en akoestische ingenieurs slaan steeds vaker de handen in elkaar om historische soundscapes opnieuw tot leven te wekken, om zo het verleden beter te kunnen beleven en begrijpen. Daar gebruiken ze verschillende bronnen voor, zoals reisdagboeken en historische foto’s. Een probleem waar ze tegen opstoten, is het feit dat het verzamelen en analyseren van deze bronnen heel veel tijd kost. Daarom ben ik in mijn thesis op zoek gegaan hoe artificiële intelligentie ons kan helpen met het automatisch terugvinden van geluidsbronnen in historische foto’s.
Hoe kan een computer “zien” wat geluid maakt?
De eerste stap in het betrouwbaar reconstrueren van een historische soundscape, is het verzamelen van voldoende bronmateriaal, bijvoorbeeld historische foto’s. Daarna moet je weten welke dingen in een foto geluid maken. Mensen zien dat meteen. Wij leggen onmiddellijk de link tussen een tram en het bijhorende geluid. Maar wat als je duizenden foto’s hebt die je moet doornemen? Dat neemt behoorlijk wat tijd in beslag. Enter artificiële intelligentie.
Een computer kan ons bij deze taak helpen via objectdetectie. Dat is een tak binnen AI waarbij een computer leert om objecten in foto’s of video’s te herkennen. Je traint een model door het zelf enkele voorbeelden te geven, waarbij je een kader tekent rond de objecten die het model later zelf moet herkennen. Objectdetectie wordt al veel gebruikt in verschillende toepassingen, denk maar aan zelfrijdende auto’s die voetgangers, verkeersborden en andere auto’s in het verkeer herkennen. Verschillende voorgetrainde modellen zijn vrij beschikbaar en kunnen verder worden getraind op de eigen data om zo de gewenste objecten te herkennen. Maar hoe werken zo’n voorgetrainde modellen nu op historische foto’s? Dat is iets wat tot nu toe nauwelijks is onderzocht.
De computer les geven
In mijn onderzoek ging ik van start met drie populaire bestaande objectdetectie modellen te testen op historische foto’s, zonder deze eerst te trainen op mijn eigen data. Op die manier kon ik achterhalen welk model in de “basisversie” het best werkte op historische foto’s. Het verschil in prestatie op moderne en historische foto’s was, zoals verwacht, groot. Historische foto’s verschillen soms sterk van moderne afbeeldingen, zo zijn ze vervaagd, korrelig of hebben een ander contrast en scherpte en bovendien zien de objecten er stilistisch ook anders uit. Een historische tram verschilt immers van een moderne tram. Voor het menselijk oog blijft het meestal duidelijk wat er afgebeeld staat, maar voor een objectdetectie model dat is voorgetraind op duizenden moderne foto’s zorgen zo’n afwijkingen ervoor dat objecten veel moeilijker te herkennen zijn.
Het best presterende model werd gekozen om verder te trainen aan de hand van historische foto’s, om zo te zorgen dat het model betrouwbaarder zou worden. Daarbij verhoogde ik kunstmatig het aantal trainingsbeelden door foto’s bijvoorbeeld donkerder, lichter, of minder scherp te maken. Hoe meer en gevarieerder de foto’s waar het model van kan leren, hoe beter het presteert. Uiteindelijk trainde ik twee modellen, één dat trams herkende en één dat koetsen kon aanduiden. Voor de trams had het model al een stevige basis, omdat die categorie van objecten ook voorkomt in de moderne datasets waarop het model oorspronkelijk werd getraind. Voor koetsen was dat helemaal anders, dat was namelijk een volledig nieuwe categorie die het model van nul moest leren herkennen. Daar ontdekte ik dat er veel meer data nodig is om tot goede resultaten te komen.
Een moderne foto in een historisch jasje
Om een computer te leren wat er op foto’s staat, is dus een hele hoop data nodig. Maar laat dat nu net een probleem zijn bij historische foto’s. Er zijn minder afbeeldingen beschikbaar en het labelen van de objecten voor de training is een heus monnikenwerk. Zou AI ons daar ook kunnen helpen?
In mijn onderzoek testte ik een bestaand model dat afbeeldingen kan omzetten in een andere stijl. Zo kon het, na training op historische en moderne afbeeldingen, een moderne foto er laten uitzien alsof hij tweehonderd jaar geleden werd genomen, en omgekeerd. Die eerste richting werkte na wat training verrassend goed. Een hedendaagse stadsfoto leek plots op een vergeelde archieffoto. Dat opent de deur om in verder onderzoek moderne datasets, waarvan er heel wat al vooraf gelabeld en vrij beschikbaar zijn, een historisch jasje te geven en deze zo te gebruiken om snel meer trainingsdata te verkrijgen. De omgekeerde weg bleek moeilijker. Het model had moeite om historische foto’s “in te kleuren”. Waar simpele en consistente elementen zoals lucht, water of bomen er wel realistisch uitzagen, struikelde het model over complexe objecten zoals mensen.
En wat hebben we vandaag geleerd?
In mijn thesis ging ik op zoek naar hoe AI onderzoekers kan helpen bij het verzamelen van data uit historische foto’s om later te gebruiken in de reconstructie van soundscapes. Als pilootproject trainde ik twee objectdetectie modellen, één om trams te herkennen en één dat koetsen kan aanduiden. Daarmee zette ik een eerste stap richting een model dat ook werkt op historische foto’s, iets wat tot nu toe nog niet bestond. Het experiment leverde veelbelovende resultaten op en toonde aan dat objectdetectie een interessante hulp kan zijn bij het verzamelen van data. Toekomstig onderzoek zou kunnen inzetten op samenwerking tussen archieven en onderzoekers, zodat één model kan worden opgebouwd dat steeds beter wordt in het herkennen van een brede waaier aan objecten in historische foto’s en op die manier wetenschappers veel tijd bespaart bij het verzamelen van data.
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