Online Personalization of Hearing Instruments

Abstract

Online personalization of hearing instruments refers to learning preferred tuning parameter values from user feedback through a control wheel (or remote control), during normal operation of the hearing aid. We perform hearing aid parameter steering by applying a linear map from acoustic features to tuning parameters. We formulate personalization of the steering parameters as the maximization of an expected utility function. A sparse Bayesian approach is then investigated for its suitability to find efficient feature representations. The feasibility of our approach is demonstrated in an application to online personalization of a noise reduction algorithm. A patient trial indicates that the acoustic features chosen for learning noise control are meaningful, that environmental steering of noise reduction makes sense, and that our personalization algorithm learns proper values for tuning parameters.