  The study of the acoustic emission of underwater gas bubbles is a subject of
both theoretical and applied interest, since it finds an important application
in the development of acoustic monitoring tools for detection and
quantification of underwater gas leakages. An underlying physical model is
essential in the study of such emissions, but is not enough: also some
statistical procedure must be applied in order to deal with all uncertainties
(including those caused by background noise). In this paper we take a
probabilistic (Bayesian) methodology which is well known in the statistical
signal analysis communitiy, and apply it to the problem of estimating the radii
of air bubbles in water. We introduce the bubblegram, a feature extraction
technique graphically similar to the traditional spectrogram but tailored to
respond only to pulse structures that correspond to a given physical model. We
investigate the performance of the bubblegram and our model in general using
laboratory generated data.
