{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:48:18Z","timestamp":1760237298640,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T00:00:00Z","timestamp":1585094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571365"],"award-info":[{"award-number":["61571365"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC1400200"],"award-info":[{"award-number":["2016YFC1400200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses\/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.<\/jats:p>","DOI":"10.3390\/e22040374","type":"journal-article","created":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T13:10:47Z","timestamp":1585141847000},"page":"374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution"],"prefix":"10.3390","volume":"22","author":[{"given":"Lei","family":"He","sequence":"first","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Xiao-Hong","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Mu-Hang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Hai-Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1121\/1.3664100","article-title":"Underwater Radiated Noise from Modern Commercial Ships","volume":"131","author":"McKenna","year":"2012","journal-title":"J. Acousti. Soc. Am."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1121\/1.4950344","article-title":"Underwater Sound Directionality of Commercial Ships","volume":"139","author":"Gassmann","year":"2016","journal-title":"J. Acousti. Soc. Am."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Xu, L., and Xu, T. (2016). Digital Underwater Acoustic Communications, Academic Press.","DOI":"10.1016\/B978-0-12-803009-7.00004-0"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TASL.2011.2125954","article-title":"Speaker Diarization: A Review of Recent Research","volume":"20","author":"Anguera","year":"2012","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/TASLP.2019.2895254","article-title":"Sound Event Detection and Time\u2013Frequency Segmentation from Weakly Labelled Data","volume":"27","author":"Kong","year":"2019","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1214\/14-AOS1245","article-title":"Wild Binary Segmentation for Multiple Change-Point Detection","volume":"42","author":"Fryzlewicz","year":"2014","journal-title":"Ann. Stat."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.eswa.2018.03.062","article-title":"Feature Evaluation for Unsupervised Bioacoustic Signal Segmentation of Anuran Calls","volume":"106","author":"Colonna","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/MSP.2004.1328092","article-title":"From Frequency to Quefrency: A History of the Cepstrum","volume":"21","author":"Oppenheim","year":"2004","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9799","DOI":"10.1016\/j.eswa.2009.02.040","article-title":"Unsupervised Speaker Segmentation with Residual Phase and MFCC Features","volume":"36","author":"Jothilakshmi","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.csl.2004.05.008","article-title":"Evaluation of BIC-Based Algorithms for Audio Segmentation","volume":"19","author":"Cettolo","year":"2005","journal-title":"Comput. Speech Lang."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1198\/jasa.2010.tm09181","article-title":"Multiple Change-Point Estimation with a Total Variation Penalty","volume":"105","author":"Harchaoui","year":"2010","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1109\/LSP.2013.2247039","article-title":"An Information-Geometric Approach to Real-Time Audio Segmentation","volume":"20","author":"Dessein","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TASL.2010.2066268","article-title":"Leveraging Kullback\u2013Leibler Divergence Measures and Information-Rich Cues for Speech Summarization","volume":"19","author":"Lin","year":"2010","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1109\/TASL.2012.2209419","article-title":"Structural Segmentation of Multitrack Audio","volume":"20","author":"Hargreaves","year":"2012","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jspi.2017.09.003","article-title":"Optimal Change Point Detection in Gaussian Processes","volume":"193","author":"Keshavarz","year":"2018","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jeconom.2018.05.003","article-title":"Simultaneous Multiple Change-Point and Factor Analysis for High-Dimensional Time Series","volume":"206","author":"Barigozzi","year":"2018","journal-title":"J. Econom."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.3390\/e14081553","article-title":"Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review","volume":"14","author":"Zanin","year":"2012","journal-title":"Entropy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2749","DOI":"10.3390\/e17052749","article-title":"Detection of Changes in Ground-Level Ozone Concentrations via Entropy","volume":"17","author":"Wu","year":"2015","journal-title":"Entropy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6212","DOI":"10.3390\/e16126212","article-title":"Ordinal Patterns, Entropy, and EEG","volume":"16","author":"Keller","year":"2014","journal-title":"Entropy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1140\/epjst\/e2013-01861-8","article-title":"Segmentation and Classification of Time Series Using Ordinal Pattern Distributions","volume":"222","author":"Sinn","year":"2013","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TSA.2005.857806","article-title":"Generalized Likelihood Ratio Test for Voiced-Unvoiced Decision in Noisy Speech Using the Harmonic Model","volume":"14","author":"Fisher","year":"2006","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Unakafov, A., and Keller, K. (2018). Change-Point Detection Using the Conditional Entropy of Ordinal Patterns. Entropy, 20.","DOI":"10.3390\/e20090709"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.cmpb.2016.02.008","article-title":"Amplitude-Aware Permutation Entropy: Illustration in Spike Detection and Signal Segmentation","volume":"128","author":"Azami","year":"2016","journal-title":"Comput. Methods Progr. Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Brandmaier, A.M. (2015). Pdc: An R Package for Complexity-Based Clustering of Time Series. J. Stat. Softw., 67.","DOI":"10.18637\/jss.v067.i05"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1140\/epjst\/e2013-01862-7","article-title":"Practical Considerations of Permutation Entropy: A Tutorial Review","volume":"222","author":"Riedl","year":"2013","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"ref_26","unstructured":"B\u00f3na, M. (2016). Combinatorics of Permutations, Chapman and Hall\/CRC."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.physa.2005.05.022","article-title":"Ordinal analysis of time series","volume":"365","author":"Keller","year":"2005","journal-title":"Phys. A"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s11222-011-9258-8","article-title":"Exact Posterior Distributions and Model Selection Criteria for Multiple Change-Point Detection Problems","volume":"22","author":"Rigaill","year":"2012","journal-title":"Stat. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107299","DOI":"10.1016\/j.sigpro.2019.107299","article-title":"Selective Review of Offline Change Point Detection Methods","volume":"167","author":"Truong","year":"2019","journal-title":"Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1214\/aos\/996986505","article-title":"Generalized Likelihood Ratio Statistics and Wilks Phenomenon","volume":"29","author":"Fan","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1214\/16-STS587","article-title":"Multiple Change-Point Detection: A Selective Overview","volume":"31","author":"Niu","year":"2016","journal-title":"Stat. Sci."},{"key":"ref_32","first-page":"1553","article-title":"Multiple Change-Point Detection via a Screening and Ranking Algorithm","volume":"23","author":"Hao","year":"2013","journal-title":"Stat. Sin."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1111\/rssb.12079","article-title":"Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation","volume":"77","author":"Cho","year":"2015","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xie, L., Xie, Y., and Xu, H. (2018). Sequential Change-Point Detection via Online Convex Optimization. Entropy, 20.","DOI":"10.3390\/e20020108"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s11222-010-9196-x","article-title":"Segmentation of the Mean of Heteroscedastic Data via Cross-Validation","volume":"21","author":"Arlot","year":"2011","journal-title":"Stat. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1080\/01621459.2013.849605","article-title":"A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data","volume":"109","author":"Matteson","year":"2014","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.apacoust.2016.06.008","article-title":"ShipsEar: An Underwater Vessel Noise Database","volume":"113","year":"2016","journal-title":"Appl. Acoust."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hubert, P., Padovese, L., and Stern, J. (2018). A Sequential Algorithm for Signal Segmentation. Entropy, 20.","DOI":"10.20944\/preprints201712.0001.v3"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.csda.2018.07.002","article-title":"New Efficient Algorithms for Multiple Change-Point Detection with Reproducing Kernels","volume":"128","author":"Celisse","year":"2018","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Giannakopoulos, T., and Pikrakis, A. (2014). Introduction to Audio Analysis: A MATLAB\u00ae Approach, Academic Press.","DOI":"10.1016\/B978-0-08-099388-1.00001-7"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"McFee, B., Raffel, C., Liang, D., Ellis, D.P., McVicar, M., Battenberg, E., and Nieto, O. (2015, January 6\u201312). Librosa: Audio and Music Signal Analysis in Python. Proceedings of the 14th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-7b98e3ed-003"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/4\/374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:11:26Z","timestamp":1760173886000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/4\/374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,25]]},"references-count":41,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["e22040374"],"URL":"https:\/\/doi.org\/10.3390\/e22040374","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2020,3,25]]}}}