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

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Acoustical Society of America
Additional Information:
Copyright 2019 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America. The following article appeared in A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation Paulo Hubert, Rebecca Killick, Alexandra Chung, and Linilson R. Padovese The Journal of the Acoustical Society of America 146:3, 1799-1807 and may be found at https://asa.scitation.org/doi/10.1121/1.5126522
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3100/3102
Subjects:
ID Code:
136384
Deposited By:
Deposited On:
28 Aug 2019 15:20
Refereed?:
Yes
Published?:
Published
Last Modified:
31 Mar 2020 06:26