Automated Detection and Identification of Blue and Fin Whale Foraging Calls by Combining Pattern Recognition and Machine Learning Techniques
A novel approach has been developed for detecting and classifying foraging calls of two mysticete species in passive acoustic recordings. This automated detector/classifier applies a computer-vision based technique, a pattern recognition method, to detect the foraging calls and remove ambient noise...
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ftnavalpschool:oai:calhoun.nps.edu:10945/68542 2024-06-09T07:37:48+00:00 Automated Detection and Identification of Blue and Fin Whale Foraging Calls by Combining Pattern Recognition and Machine Learning Techniques Huang, Ho Chun Huan, Ming Jer Joseph, John Margolina, Tetyana Oceanography 2016 7 p. application/pdf https://hdl.handle.net/10945/68542 unknown Huang, Ho Chun, et al. "Automated detection and identification of blue and fin whale foraging calls by combining pattern recognition and machine learning techniques." OCEANS 2016 MTS/IEEE Monterey. IEEE, 2016. https://hdl.handle.net/10945/68542 This 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. whale pattern recognition machine learning foraging call Article 2016 ftnavalpschool 2024-05-15T00:27:01Z A novel approach has been developed for detecting and classifying foraging calls of two mysticete species in passive acoustic recordings. This automated detector/classifier applies a computer-vision based technique, a pattern recognition method, to detect the foraging calls and remove ambient noise effects. The detected calls were then classified as blue whale D-calls [1] or fin whale 40-Hz calls [2] using a logistic regression classifier, a machine learning technique. The detector/classifier has been trained using the 2015 Detection, Classification, Localization and Density Estimation (DCLDE 2015, Scripps Institution of Oceanography UCSD [3]) low-frequency annotated set of passive acoustic data, collected in the Southern California Bight, and its out-of-sample performance was estimated by using a cross-validation technique. The DCLDE 2015 scoring tool was used to estimate the detector/classifier performance in a standardized way. The pattern recognition algorithm’s out-of-sample performance was scored as 96.68% recall with 92.03 % precision. The machine learning algorithm’s out-of-sample prediction accuracy was 95.20%. The result indicated the potential of this detector/classifier on real-time passive acoustic marine mammal monitoring and bioacoustics signal processing. This research was supported by the US Navy’s Living Marine Resources (LMR) Program. Article in Journal/Newspaper Acoustic & Marine Mammal Monitoring Blue whale Fin whale Marine Mammal Monitoring Naval Postgraduate School: Calhoun Scripps ENVELOPE(-63.783,-63.783,-69.150,-69.150) |
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Open Polar |
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Naval Postgraduate School: Calhoun |
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ftnavalpschool |
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topic |
whale pattern recognition machine learning foraging call |
spellingShingle |
whale pattern recognition machine learning foraging call Huang, Ho Chun Huan, Ming Jer Joseph, John Margolina, Tetyana Automated Detection and Identification of Blue and Fin Whale Foraging Calls by Combining Pattern Recognition and Machine Learning Techniques |
topic_facet |
whale pattern recognition machine learning foraging call |
description |
A novel approach has been developed for detecting and classifying foraging calls of two mysticete species in passive acoustic recordings. This automated detector/classifier applies a computer-vision based technique, a pattern recognition method, to detect the foraging calls and remove ambient noise effects. The detected calls were then classified as blue whale D-calls [1] or fin whale 40-Hz calls [2] using a logistic regression classifier, a machine learning technique. The detector/classifier has been trained using the 2015 Detection, Classification, Localization and Density Estimation (DCLDE 2015, Scripps Institution of Oceanography UCSD [3]) low-frequency annotated set of passive acoustic data, collected in the Southern California Bight, and its out-of-sample performance was estimated by using a cross-validation technique. The DCLDE 2015 scoring tool was used to estimate the detector/classifier performance in a standardized way. The pattern recognition algorithm’s out-of-sample performance was scored as 96.68% recall with 92.03 % precision. The machine learning algorithm’s out-of-sample prediction accuracy was 95.20%. The result indicated the potential of this detector/classifier on real-time passive acoustic marine mammal monitoring and bioacoustics signal processing. This research was supported by the US Navy’s Living Marine Resources (LMR) Program. |
author2 |
Oceanography |
format |
Article in Journal/Newspaper |
author |
Huang, Ho Chun Huan, Ming Jer Joseph, John Margolina, Tetyana |
author_facet |
Huang, Ho Chun Huan, Ming Jer Joseph, John Margolina, Tetyana |
author_sort |
Huang, Ho Chun |
title |
Automated Detection and Identification of Blue and Fin Whale Foraging Calls by Combining Pattern Recognition and Machine Learning Techniques |
title_short |
Automated Detection and Identification of Blue and Fin Whale Foraging Calls by Combining Pattern Recognition and Machine Learning Techniques |
title_full |
Automated Detection and Identification of Blue and Fin Whale Foraging Calls by Combining Pattern Recognition and Machine Learning Techniques |
title_fullStr |
Automated Detection and Identification of Blue and Fin Whale Foraging Calls by Combining Pattern Recognition and Machine Learning Techniques |
title_full_unstemmed |
Automated Detection and Identification of Blue and Fin Whale Foraging Calls by Combining Pattern Recognition and Machine Learning Techniques |
title_sort |
automated detection and identification of blue and fin whale foraging calls by combining pattern recognition and machine learning techniques |
publishDate |
2016 |
url |
https://hdl.handle.net/10945/68542 |
long_lat |
ENVELOPE(-63.783,-63.783,-69.150,-69.150) |
geographic |
Scripps |
geographic_facet |
Scripps |
genre |
Acoustic & Marine Mammal Monitoring Blue whale Fin whale Marine Mammal Monitoring |
genre_facet |
Acoustic & Marine Mammal Monitoring Blue whale Fin whale Marine Mammal Monitoring |
op_relation |
Huang, Ho Chun, et al. "Automated detection and identification of blue and fin whale foraging calls by combining pattern recognition and machine learning techniques." OCEANS 2016 MTS/IEEE Monterey. IEEE, 2016. https://hdl.handle.net/10945/68542 |
op_rights |
This 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. |
_version_ |
1801373293423362048 |