Rights statement: 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
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation
AU - Hubert, Paulo
AU - Killick, Rebecca
AU - Chung, Alexandra
AU - Padovese, Linilson
N1 - 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
PY - 2019/9/27
Y1 - 2019/9/27
N2 - 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.
AB - 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.
U2 - 10.1121/1.5126522
DO - 10.1121/1.5126522
M3 - Journal article
VL - 146
JO - Journal of the Acoustical Society of America
JF - Journal of the Acoustical Society of America
SN - 0001-4966
M1 - 1799
ER -