A Bayesian binary algorithm for RMS-based acoustic signal segmentation
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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.
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