[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126806-en":3,"doc-seo-126806-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126806,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Classification of Surface Vessels - Underwater Acoustic Data and Machine Learning - Thesis","Automatic vessel classification is a high-value research problem for the U.S. Navy, using underwater acoustic signatures to identify maritime platforms. The thesis evaluates three machine learning approaches—Gaussian mixture models, random forest, and k-nearest neighbors—on the ShipsEar database. Mel-frequency cepstrum coefficients are used as feature representations and are compared with prior results reported for the ShipsEar noise database in Applied Acoustics. Findings show that all three methods provide feasible classification performance, with Gaussian mixture models delivering notable improvements over earlier outcomes.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| NPS Scholarship | Theses |\n| --- | --- |\n\n2023-09  \nCLASSIFICATION OF SURFACE VESSELS USING  \nUNDERWATER ACOUSTIC DATA AND MACHINE LEARNING  \nHenderson, John M.  \nMonterey, CA; Naval Postgraduate School  \n[https://hdl.handle.net/10945/72356](https://hdl.handle.net/10945/72356)  \nThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.  \nDownloaded from NPS Archive: Calhoun  \nNAVAL POSTGRADUATE  \nSCHOOL MONTEREY, CALIFORNIA  \nTHESIS  \nCLASSIFICATION OF SURFACE VESSELS USING UNDERWATER ACOUSTIC  \nDATA AND MACHINE LEARNING  \nby  \nJohn M. Henderson  \nSeptember 2023  \nThesis Advisor: Monique P. Fargues  \nCo-Advisor: Kay L. Gemba  \nApproved for public release. Distribution is unlimited.  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  |  | Form Approved OMB No. 0704-0188 |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. |  |  |  |  |  |  |  |\n| 1. AGENCY USE ONLY (Leave blank) |  | 2. REPORT DATE\u003Cbr>September 2023 |  | 3. REPORT TYPE AND DATES COVERED\u003Cbr>Master’s thesis |  |  |  |\n| 4. TITLE AND SUBTITLE\u003Cbr>CLASSIFICATION OF SURFACE VESSELS USING UNDERWATER ACOUSTIC DATA AND MACHINE LEARNING |  |  |  |  |  | 5. FUNDING NUMBERS |  |\n| 6. AUTHOR(S) John M. Henderson |  |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  |  | 8. PERFORMING\u003Cbr>ORGANIZATION REPORT NUMBER |  |\n| 9. SPONSORING / MONITORING AGENCY NAME(S) AND\u003Cbr>ADDRESS(ES)\u003Cbr>N/A |  |  |  |  |  | 10. SPONSORING / MONITORING AGENCY REPORT NUMBER |  |\n| 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. |  |  |  |  |  |  |  |\n| 12a. DISTRIBUTION / AVAILABILITY STATEMENT\u003Cbr>Approved for public release. Distribution is unlimited. |  |  |  |  |  | 12b. DISTRIBUTION CODE\u003Cbr>A |  |\n| 13. ABSTRACT (maximum 200 words)\u003Cbr>Automatic vessel classification is a highly relevant research topic, particularly for the U.S. Navy. In this study, we consider three machine learning techniques to classify maritime vessels based on their underwater noise: Gaussian mixture models, random forest, and k-nearest neighbors. The ShipsEar database, developed by Santos-Domínguez et al., was used to conduct the study. Mel-frequency cepstrum coefficients were selected for class feature characteristics to compare with previous findings presented by Santos-Domínguez et [al. in](al. in) their publication titled “ShipsEar: An Underwater Vessel Noise database” published in the Applied Acoustics journal, volume 113. Results indicate that all three methods offer a feasible solution to the classification problem. Notably, Gaussian mixture models show significant performance improvements over results achieved by Santos-Domínguez et al. |  |  |  |  |  |  |  |\n| 14. SUBJECT TERMS\u003Cbr>machine learning, AI, SONAR, ShipsEar, ML, GMM |  |  |  |  |  |  | 15. NUMBER OF PAGES\u003Cbr>79 |\n|  |  |  |  |  |  |  | 16. PRICE CODE |\n| 17. SECURITY\u003Cbr>CLASSIFICATION OF REPORT\u003Cbr>Unclassified | 18. 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