Nielsen, Jens Brehm

Nutidens digitale apparater kan skræddersys betydeligt til den enkelte bruger ved justering eller optimering af en række parametre, der påvirker apparatets output. Personalisering referer til sådan en form for personlig optimering. For høreapparater er personalisering ikke kun et tilbud til brugeren, men en nødvendighed, hvis brugeren skal opnå det fulde udbytte af nutidens høreapparater indeholdende flere parametre. I dag bliver personalisering stadig baseret på manuelt at prøve sig frem, hvilket er tidskrævende. Det bliver gjort af brugeren selv, eller hvad angår høreapparater, af en professionel høreapparatsspecialist baseret på typisk uklar mundtlig feedback fra brugeren. Dette resulterer i ofte ikke optimale eller sågar uhensigtsmæssige apparatindstillinger. Denne afhandling præsenterer forskning omkring et machine-learning-baseret personaliseringssystem til at forbedre personaliseringen af apparater specielt med henblik på høreapparater. Det foreslåede personaliseringssystem lærer iterativt en ikkeparametrisk probabilistisk model af en brugers (antaget) interne responsfunktion over mulige parameterindstillinger baseret direkte på perceptuel feedback fra brugeren. Et sekventielt design baseret på active learning bruges for i så få iterationer som muligt at lære hvilken indstilling, der maksimerer brugerens interne responsfunktion. I udførte eksperimenter lærte det foreslåede personaliseringssystem en signifikant foretrukket indstilling for individuelle brugere indenfor ti til tyve iterationer i scenarier med op til fire parametre. Efter en kort introduktion, der inkluderer en oversigt over resultater og forskningsbidrag, fokuserer det første hovedkapitel på den probabilistiske modelleringsmetode, hvor en Gaussisk proces bruges til modellering af brugerens ikke observerede interne responsfunktion. De adresserede hovedudfordringer er i denne kontekst at tage højde for inkonsistent og dermed støjfyldt brugerfeedback og at supportere feedback, som nøje reflekterer brugerens perception uden dog at resultere i en høj kognitiv belastning. I det andet hovedkapitel bliver active learning og sekventielt design diskuteret i relation til udfordringen i at lære i så få iterationer som mulig den indstilling, der maksimerer brugerens ikke observerede interne responsfunktion. I relation til en Gaussisk proces foreslås et active learning kriterium, som er specifikt velegnet til den omtalte form for optimering. Det sidste kapitel indeholder en overordnet diskussion om det her omtale stykke arbejde og forskning baseret til dels på de otte videnskabelige artikler, som er at finde i appendikserne. Det sidste kapitel indeholder også konklusionen.Today, modern digital devices can be customized significantly to the individual user by adjusting or optimizing multiple parameters affecting the output of the devices. Such personal optimization of devices is referred to as personalization. In the case of hearing aids, personalization is not only a possibility offered to the user, but a requirement that must be performed carefully and precisely in order for the user to utilize the full potential of modern multi-parameter hearing aids. Today though, personalization is still based on a manual timeconsuming trial-and-error approach performed by the user himself or, in case of hearing aids, by a hearing-care professional based on typically ambiguous oral feedback from the user. This often results in sub-optimal or even inappropriate settings of multi-parameter devices. This dissertation presents research on a machine-learning based interactive personalization system to improve the personalization of devices and, in particular, of hearing-aid devices. The proposed personalization system iteratively learns a non-parametric probabilistic model of a user’s assumed internal response function over all possible settings of a multi-parameter device based directly on sequential perceptual feedback from the user. A sequential design based on active learning is used to obtain the maximum of the user’s unknown internal response function in as few iterations as possible. Experiments were conducted where the proposed personalization system obtained a significantly preferred setting for individual users within ten to twenty iterations in scenarios with up to four parameters. Following a short introduction that includes a summary of results and contributions, the first main chapter focuses on the probabilistic modeling framework in which a Gaussian process is used to model the user’s unobserved internal response function. The first main challenge addressed in this context is to account for inconsistent and thus noisy user feedback. The second main challenge addressed is to support feedback which closely reflects the user’s perception while providing maximal information about it without imposing a high cognitive load. In the second main chapter, active learning and sequential design are discussed in relation to the challenge of obtaining the setting that maximizes the user’s unobserved internal response function in as few iterations as possible. For the Gaussian process framework, an active learning criterion is proposed specifically suitable for this type of optimization. The final chapter contains an overall discussion and conclusion of the present work and research based in part on the results from eight scientific paper contributions contained in the appendices

Online Research Database In Technology

Systems for Personalization of Hearing Instruments:A Machine Learning Approach

http://orbit.dtu.dk/ws/files/108304925/phd325_Nielsen_JB.pdf

Systems for Personalization of Hearing Instruments:A Machine Learning Approach

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