Hubert Paulo, Padovese Linilson, Stern Julio Michael
Instituto de Matemática e Estatística, University of São Paulo (IME-USP), São Paulo 05508-090, Brazil.
Mechanical Engineering Department, Escola Politécnica-University of São Paulo (EP-USP), São Paulo 05508-010, Brazil.
Entropy (Basel). 2018 Jan 12;20(1):55. doi: 10.3390/e20010055.
The problem of event detection in general noisy signals arises in many applications; usually, either a functional form of the event is available, or a previous annotated sample with instances of the event that can be used to train a classification algorithm. There are situations, however, where neither functional forms nor annotated samples are available; then, it is necessary to apply other strategies to separate and characterize events. In this work, we analyze 15-min samples of an acoustic signal, and are interested in separating sections, or segments, of the signal which are likely to contain significant events. For that, we apply a sequential algorithm with the only assumption that an event alters the energy of the signal. The algorithm is entirely based on Bayesian methods.
在许多应用中都会出现从一般噪声信号中检测事件的问题；通常，要么事件的函数形式是已知的，要么有一个带有事件实例的先前注释样本，可用于训练分类算法。然而，在某些情况下，既没有函数形式也没有注释样本；那么，就有必要应用其他策略来分离和表征事件。在这项工作中，我们分析了一段15分钟的声学信号样本，并希望分离出信号中可能包含重要事件的部分或片段。为此，我们应用了一种顺序算法，唯一的假设是事件会改变信号的能量。该算法完全基于贝叶斯方法。
