: Received: 29 November 2017 / Approved: 1 December 2017 / Online: 1 December 2017 (06:37:39 CET)
: Received: 15 December 2017 / Approved: 17 December 2017 / Online: 17 December 2017 (08:58:58 CET)
: Received: 8 January 2018 / Approved: 8 January 2018 / Online: 8 January 2018 (18:29:11 CET)
Hubert, P.; Padovese, L.; Stern, J.M. A Sequential Algorithm for Signal Segmentation. Entropy2018, 20, 55.
Hubert, P.; Padovese, L.; Stern, J.M. A Sequential Algorithm for Signal Segmentation. Entropy 2018, 20, 55.
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 an acoustic signal obtained from a hydrophone, 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 average power of the signal. The algorithm is entirely based on bayesian methods, and shows a very promising performance in detecting either short or long events.
signal processing; bayesian methods; subaquatic audio; hydrophone; unsupervised learning
MATHEMATICS & COMPUTER SCIENCE, Probability and Statistics
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.