A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation

Hubert, Paulo and Killick, Rebecca and Chung, Alexandra and Padovese, Linilson (2019) A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation. Journal of the Acoustical Society of America, 146. ISSN 0001-4966

Text (Manuscript)
Manuscript.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.
Download (876kB)

Abstract

Changepoint analysis (also known as segmentation analysis) aims to analyze 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. This work is interested in an algorithm for the segmentation of long duration acoustic signals; the segmentation is based on the change of the root-mean-square power of the signal. It investigates a Bayesian model with two possible parameterizations and proposes a binary algorithm in two versions using non-informative or informative priors. These algorithms are tested in the segmentation of annotated acoustic signals from the Alcatrazes marine preservation park in Brazil.