A Bayesian binary algorithm for RMS-based acoustic signal segmentation

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 vari...

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Bibliographic Details
Published inarXiv.org
Main Authors Hubert, Paulo, Killick, Rebecca, Chung, Alexandra, Padovese, Linilson
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.08.2019
Subjects
Acoustics
Algorithms
Bayesian analysis
Regression analysis
Segmentation
Statistics - Applications
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1902.06315