  The pure tone hearing threshold is usually estimated from responses to
stimuli at a set of standard frequencies. This paper describes a probabilistic
approach to the estimation problem in which the hearing threshold is modelled
as a smooth continuous function of frequency using a Gaussian process. This
allows sampling at any frequency and reduces the number of required
measurements. The Gaussian process is combined with a probabilistic response
model to account for uncertainty in the responses. The resulting full model can
be interpreted as a two-dimensional binary classifier for stimuli, and provides
uncertainty bands on the estimated threshold curve. The optimal next stimulus
is determined based on an information theoretic criterion. This leads to a
robust adaptive estimation method that can be applied to fully automate the
hearing threshold estimation process.
