A Probabilistic Modeling Approach to Hearing Loss Compensation
This addresses hearing loss compensation for patients, but it is incremental as it builds on existing probabilistic methods applied to a specific domain.
The paper tackles the lack of a fundamental theory for hearing aid fitting, which affects about 20% of patients, by proposing a probabilistic modeling approach that automates algorithm design, fitting, and evaluation using factor graphs.
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.