Article
Version 2
Preserved in Portico This version is not peer-reviewed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.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
