  Changepoint analysis (also known as segmentation analysis) aims at analyzing
an ordered, one-dimensional vector, in order to find locations where some
characteristic of the data changes. Many models and algorithms have been
studied under this theme, including models for changes in mean and / or
variance, changes in linear regression parameters, etc. In this work, we are
interested in an algorithm for the segmentation of long duration acoustic
signals; the segmentation is based on the change of the RMS power of the
signal. We investigate a Bayesian model with two possible parameterizations,
and propose a binary algorithm in two versions, using non-informative or
informative priors. We apply our algorithm to the segmentation of annotated
acoustic signals from the Alcatrazes marine preservation park in Brazil.
