A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation
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,...
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| Vydáno v: | The Journal of the Acoustical Society of America Ročník 146; číslo 3; s. 1799 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
01.09.2019
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| ISSN: | 1520-8524, 1520-8524 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
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| Shrnutí: | 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. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1520-8524 1520-8524 |
| DOI: | 10.1121/1.5126522 |