  Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the
impairment of each specific patient. The lack of a fundamental HA fitting
theory is a strong contributing factor to an unsatisfying sound experience for
about 20% of hearing aid patients. This paper proposes a probabilistic modeling
approach to the design of HA algorithms. The proposed method relies on a
generative probabilistic model for the hearing loss problem and provides for
automated inference of the corresponding (1) signal processing algorithm, (2)
the fitting solution as well as a principled (3) performance evaluation metric.
All three tasks are realized as message passing algorithms in a factor graph
representation of the generative model, which in principle allows for fast
implementation on hearing aid or mobile device hardware. The methods are
theoretically worked out and simulated with a custom-built factor graph toolbox
for a specific hearing loss model.
