Detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques
A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and...
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Monterey, California. Naval Postgraduate School
2016
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ftnavalpschool:oai:calhoun.nps.edu:10945/51720 2024-06-09T07:44:56+00:00 Detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques Huang, Ho-Chun Joseph, John Margolina, Tetyana Oceanography 2016-12 application/pdf https://hdl.handle.net/10945/51720 unknown Monterey, California. Naval Postgraduate School https://hdl.handle.net/10945/51720 Copyright is reserved by the copyright owner. blue whale fin whale foraging call pattern recognition machine learning logistic regression classifier detection classification Thesis 2016 ftnavalpschool 2024-05-15T00:55:15Z A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and to detect the foraging calls. The detected calls are then classified as blue whale D-calls or fin whale 40-Hz calls using a machine learning technique, a logistic regression classifier. These algorithms have been trained and evaluated using the Detection, Classification, Localization, and Density Estimation (DCLDE) annotated passive acoustic data, which were recorded off the Central and Southern California coast from 2009 to 2013. By using the cross-validation method and DCLDE scoring tool, this research shows high out-of-sample performance for these algorithms, namely 96% recall with 92% precision for pattern recognition and 96% accuracy for the logistic regression classifier. The result was published by the Institute of Electrical and Electronics Engineers (2016). The advantages of this automated approach over traditional manual methods are reproducibility, known performance, cost-efficiency, and automation. This approach has the potential to conquer the challenges of detecting and classifying the foraging calls, including the analysis of large acoustic data sets and real-time acoustic data processing. Approved for public release; distribution is unlimited. Lieutenant Commander, Republic of China Navy http://archive.org/details/detectionndclass1094551720 Thesis Balaenoptera musculus Balaenoptera physalus baleen whale Blue whale Fin whale Naval Postgraduate School: Calhoun |
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Naval Postgraduate School: Calhoun |
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blue whale fin whale foraging call pattern recognition machine learning logistic regression classifier detection classification |
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blue whale fin whale foraging call pattern recognition machine learning logistic regression classifier detection classification Huang, Ho-Chun Detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques |
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blue whale fin whale foraging call pattern recognition machine learning logistic regression classifier detection classification |
description |
A three-step approach has been developed for detecting and classifying the foraging calls of the blue whale, Balaenoptera musculus, and fin whale, Balaenoptera physalus, in passive acoustic recordings. This approach includes a pattern recognition algorithm to reduce the effects of ambient noise and to detect the foraging calls. The detected calls are then classified as blue whale D-calls or fin whale 40-Hz calls using a machine learning technique, a logistic regression classifier. These algorithms have been trained and evaluated using the Detection, Classification, Localization, and Density Estimation (DCLDE) annotated passive acoustic data, which were recorded off the Central and Southern California coast from 2009 to 2013. By using the cross-validation method and DCLDE scoring tool, this research shows high out-of-sample performance for these algorithms, namely 96% recall with 92% precision for pattern recognition and 96% accuracy for the logistic regression classifier. The result was published by the Institute of Electrical and Electronics Engineers (2016). The advantages of this automated approach over traditional manual methods are reproducibility, known performance, cost-efficiency, and automation. This approach has the potential to conquer the challenges of detecting and classifying the foraging calls, including the analysis of large acoustic data sets and real-time acoustic data processing. Approved for public release; distribution is unlimited. Lieutenant Commander, Republic of China Navy http://archive.org/details/detectionndclass1094551720 |
author2 |
Joseph, John Margolina, Tetyana Oceanography |
format |
Thesis |
author |
Huang, Ho-Chun |
author_facet |
Huang, Ho-Chun |
author_sort |
Huang, Ho-Chun |
title |
Detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques |
title_short |
Detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques |
title_full |
Detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques |
title_fullStr |
Detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques |
title_full_unstemmed |
Detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques |
title_sort |
detection and classification of baleen whale foraging calls combining pattern recognition and machine learning techniques |
publisher |
Monterey, California. Naval Postgraduate School |
publishDate |
2016 |
url |
https://hdl.handle.net/10945/51720 |
genre |
Balaenoptera musculus Balaenoptera physalus baleen whale Blue whale Fin whale |
genre_facet |
Balaenoptera musculus Balaenoptera physalus baleen whale Blue whale Fin whale |
op_relation |
https://hdl.handle.net/10945/51720 |
op_rights |
Copyright is reserved by the copyright owner. |
_version_ |
1801373830566903808 |