Article
Version 3
Preserved in Portico This version is not peer-reviewed
A Sequential Algorithm for Signal Segmentation
Version 1
: Received: 29 November 2017 / Approved: 1 December 2017 / Online: 1 December 2017 (06:37:39 CET)
Version 2 : Received: 15 December 2017 / Approved: 17 December 2017 / Online: 17 December 2017 (08:58:58 CET)
Version 3 : Received: 8 January 2018 / Approved: 8 January 2018 / Online: 8 January 2018 (18:29:11 CET)
Version 2 : Received: 15 December 2017 / Approved: 17 December 2017 / Online: 17 December 2017 (08:58:58 CET)
Version 3 : Received: 8 January 2018 / Approved: 8 January 2018 / Online: 8 January 2018 (18:29:11 CET)
A peer-reviewed article of this Preprint also exists.
Hubert, P.; Padovese, L.; Stern, J.M. A Sequential Algorithm for Signal Segmentation. Entropy 2018, 20, 55. Hubert, P.; Padovese, L.; Stern, J.M. A Sequential Algorithm for Signal Segmentation. Entropy 2018, 20, 55.
Abstract
The problem of event detection in general noisy signals arises in many applications; usually, either a functional form for 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 minute-long samples of an acoustic signal, and are interested in separating sections, or segments, of the signal which are likely to contain significative 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.
Keywords
signal processing; bayesian methods; subaquatic audio; hydrophone; unsupervised learning
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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