Department of Mechanical Engineering, Escola Politecnica, University of São Paulo, São Paulo, SP, Brazil.
Mathematics and Statistics Department, Lancaster University, Fylde Avenue, Bailrigg, Lancaster, LA1 4YW, United Kingdom.
J Acoust Soc Am. 2019 Sep;146(3):1799. doi: 10.1121/1.5126522.
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.
变点分析（也称为分段分析）旨在分析有序的一维向量，以找到数据特征发生变化的位置。许多模型和算法都在这一主题下进行了研究，包括均值和/或方差变化的模型、线性回归参数变化的模型等。本工作感兴趣的是一种用于长持续时间声学信号分段的算法；分段基于信号均方根功率的变化。它研究了一种具有两种可能参数化的贝叶斯模型，并提出了两种使用非信息或信息先验的二进制算法。这些算法在巴西阿尔卡特拉斯海洋保护区的注释声学信号的分段中进行了测试。
