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A Sequential Algorithm for Signal Segmentation

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
Author to whom correspondence should be addressed.
Entropy 2018, 20(1), 55;
Received: 29 November 2017 / Revised: 8 January 2018 / Accepted: 9 January 2018 / Published: 12 January 2018
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. View Full-Text
Keywords: signal detection; bayesian methods; hypothesis testing; audio segmentation signal detection; bayesian methods; hypothesis testing; audio segmentation
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MDPI and ACS Style

Hubert, P.; Padovese, L.; Stern, J.M. A Sequential Algorithm for Signal Segmentation. Entropy 2018, 20, 55.

AMA Style

Hubert P, Padovese L, Stern JM. A Sequential Algorithm for Signal Segmentation. Entropy. 2018; 20(1):55.

Chicago/Turabian Style

Hubert, Paulo, Linilson Padovese, and Julio M. Stern 2018. "A Sequential Algorithm for Signal Segmentation" Entropy 20, no. 1: 55.

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