  Personalization of the amplification function of hearing aids has been shown
to be of benefit to hearing aid users in previous studies. Several machine
learning-based personalization approaches have been introduced in the
literature. This paper presents a machine learning personalization approach
with the advantage of being efficient in its training based on paired
comparisons which makes it practical and field deployable. The training
efficiency of this approach is the result of treating frequency bands
independent of one another and by simultaneously carrying out Bayesian machine
learning in each band across all of the frequency bands. Simulation results
indicate that this approach leads to an estimated hearing preference function
close to the true hearing preference function in fewer number of paired
comparisons relative to the previous machine learning approaches. In addition,
a clinical experiment conducted on eight subjects with hearing impairment
indicate that this training efficient personalization approach provides
personalized gain settings which are on average six times more preferred over
the standard prescriptive gain settings.
