  In this work we discuss the problem of identifying sound sources from
pressure measurements with a Bayesian approach. The acoustics are modelled by
the Helmholtz equation and the goal is to get information about the number,
strength and position of the sound sources, under the assumption that
measurements of the acoustic pressure are noisy. We propose a problem specific
prior distribution of the number, the amplitudes and positions of the sound
sources and algorithms to compute an approximation of the associated posterior.
We also discuss a finite element discretization of the Helmholtz equation for
the practical computation and prove convergence rates of the resulting
discretized posterior to the true posterior. The theoretical results are
illustrated by numerical experiments, which indicate that the proven rates are
sharp.
