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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Hubert, Paulo [1, 2] ; Killick, Rebecca [3] ; Chung, Alexandra [4] ; Padovese, Linilson R. [4]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Dept Mech Engn, Escola Politecn, Sao Paulo, SP - Brazil
[2] Technol & Data Sci Dept, John F Kennedy Bldg, Ave Nove de Julho 2029, BR-01313902 Sao Paulo, SP - Brazil
[3] Univ Lancaster, Math & Stat Dept, Fylde Ave, Lancaster LA1 4YW - England
[4] Univ Sao Paulo, Mech Engn Dept, Escola Politecn, Ave Prof Mello Moraes 2231, BR-05508030 Sao Paulo, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Source: JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA; v. 146, n. 3, p. 1799-1807, SEP 2019.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 14/50279-4 - Brasil Research Centre for Gas Innovation
Grantee:Julio Romano Meneghini
Support type: Research Grants - Research Centers in Engineering Program
FAPESP's process: 16/02175-0 - Underwater Soundscapes in the São Paulo State Coast
Grantee:Linilson Rodrigues Padovese
Support type: Regular Research Grants