{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:58:29Z","timestamp":1777892309480,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,12]],"date-time":"2018-01-12T00:00:00Z","timestamp":1515715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"publisher","award":["16\/02175-0"],"award-info":[{"award-number":["16\/02175-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"publisher","award":["2013\/07375-0"],"award-info":[{"award-number":["2013\/07375-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"publisher","award":["2014\/50279-4"],"award-info":[{"award-number":["2014\/50279-4"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"CNPq","doi-asserted-by":"publisher","award":["308405\/2013-7"],"award-info":[{"award-number":["308405\/2013-7"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"CNPq","doi-asserted-by":"publisher","award":["301206\/2011-2"],"award-info":[{"award-number":["301206\/2011-2"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e20010055","type":"journal-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T04:01:55Z","timestamp":1515988915000},"page":"55","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Sequential Algorithm for Signal Segmentation"],"prefix":"10.3390","volume":"20","author":[{"given":"Paulo","family":"Hubert","sequence":"first","affiliation":[{"name":"Instituto de Matem\u00e1tica e Estat\u00edstica, University of S\u00e3o Paulo (IME-USP), S\u00e3o Paulo 05508-090, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linilson","family":"Padovese","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Escola Polit\u00e9cnica\u2014University of S\u00e3o Paulo (EP-USP), S\u00e3o Paulo 05508-010, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2720-3871","authenticated-orcid":false,"given":"Julio","family":"Stern","sequence":"additional","affiliation":[{"name":"Instituto de Matem\u00e1tica e Estat\u00edstica, University of S\u00e3o Paulo (IME-USP), S\u00e3o Paulo 05508-090, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Etter, P.C. 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Reson."},{"key":"ref_6","first-page":"552","article-title":"Bayesian Analysis. II. Signal Detection and Model Selection","volume":"88","author":"Bretthorst","year":"1990","journal-title":"J. Magn. Reson."},{"key":"ref_7","first-page":"571","article-title":"Bayesian Analysis. III. Applications to NMR Signal Detection, Model Selection and Parameter Estimation","volume":"88","author":"Bretthorst","year":"1990","journal-title":"J. Magn. Reson."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hubert, P., Padovese, L.R., and Stern, J.M. (2017). Full bayesian approach for signal detection with an application to boat detection on underwater soundscape data. Maximum Entropy Methods in Science and Engineering, Springer-Verlag.","DOI":"10.1007\/978-3-319-91143-4_19"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"259","DOI":"10.2478\/amcs-2014-0019","article-title":"Automatic Speech Signal Segmentation Based on the Innovation Adaptive Filter","volume":"24","author":"Makowsky","year":"2014","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ukil, A., and Zivanovic, R. (2006, January 10\u201312). Automatic Signal Segmentation based on Abrupt Change Detection for Power Systems Applications. Proceedings of the Power India Conference, New Delhi, India.","DOI":"10.1109\/POWERI.2006.1632488"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1214\/11-AOS943","article-title":"Multiple Testing of Local Maxima for Detection of Peaks in 1D","volume":"39","author":"Schwartzman","year":"2011","journal-title":"Ann. Stat."},{"key":"ref_12","first-page":"19","article-title":"An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals","volume":"97","author":"Kuntamalla","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_13","first-page":"1","article-title":"An Overview of Automatic Audio Segmentation","volume":"11","author":"Thedorou","year":"2014","journal-title":"Int. J. Inf. Technol. Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1109\/PROC.1982.12425","article-title":"On the Rationale of Maximum-Entropy Methods","volume":"70","author":"Jaynes","year":"1982","journal-title":"IEEE Proc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TSSC.1968.300117","article-title":"Prior Probabilities","volume":"4","author":"Jaynes","year":"1968","journal-title":"IEEE Trans. Syst. Sci. Cybern."},{"key":"ref_16","first-page":"453","article-title":"An Invariant Form for the Prior Probability in Estimation Problems","volume":"Volume 186","author":"Jeffreys","year":"1946","journal-title":"Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1080\/01621459.1992.10475256","article-title":"The Bayes\/Non-bayes compromise: A Brief Review","volume":"87","author":"Good","year":"1992","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.spl.2013.10.018","article-title":"Changing Statistical Significance with the Amount of Information: The Adaptative \u03b1 Significance Level","volume":"85","author":"Perez","year":"2014","journal-title":"Stat. Probab. Lett."},{"key":"ref_19","first-page":"159","article-title":"On the Concept of P-value","volume":"7","author":"Pereira","year":"1993","journal-title":"Revis. Bras. Probab. Estat."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"99","DOI":"10.3390\/e1040099","article-title":"Evidence and credibility: Full Bayesian significance test for precise hypotheses","volume":"1","author":"Pereira","year":"1999","journal-title":"Entropy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1080\/01621459.2016.1240684","article-title":"A New Bayesian Test to Test for the Intractability-Countering Hypothesis","volume":"112","author":"Chakrabarty","year":"2017","journal-title":"JASA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1063\/1.3275617","article-title":"FBST for Generalized Poisson Distributions","volume":"1193","author":"Hubert","year":"2009","journal-title":"AIP Conf. Procee."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1590\/S1415-47572009000300028","article-title":"A Straightforward Multiallelic Significance Test for the Hardy-Weinberg Equilibrium Law","volume":"32","author":"Lauretto","year":"2009","journal-title":"Genet. Mol. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/S0167-7152(02)00314-0","article-title":"Testing the Independence of Poisson Variates Under the Holgate Bivariate Distribution: The Power of a New Evidence Test","volume":"60","author":"Stern","year":"2002","journal-title":"Stat. Probab. Lett."},{"key":"ref_25","unstructured":"Palshikar, G.K. (2009, January 6\u20137). Simple Algorithms for Peak Detection in Time Series. Proceedings of the 1st International Conference Advanced Data Analysis, Business Analytics and Intelligence, Ahmedabad, India."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"A1339","DOI":"10.1137\/130932831","article-title":"Approximate Bayesian Computation by Subset Simulation","volume":"36","author":"Chiachio","year":"2014","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_27","unstructured":"Beck, J.L., and Zuev, K.M. (2014, January 13\u201316). Asymptotically independent Markov Sampling: A new MCMC scheme for Bayesian inference. Proceedings of the Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty Modeling and Analysis (ISUMA), Liverpool, UK."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1615\/Int.J.UncertaintyQuantification.2012003641","article-title":"Prior and Posterior Robust Stochastic Predictions for Dynamical Systems Using Probability Logic","volume":"3","author":"Beck","year":"2014","journal-title":"Int. J. Uncertain. Quantif."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.ymssp.2016.09.035","article-title":"A Multilevel Bayesian Method for Ultrasound-Based Damage Identification in Composites Laminates","volume":"88","author":"Chiachio","year":"2017","journal-title":"Mech. Syst. 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