Research Article Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform
The study of cetacean vocalizations is usually based on spectrogram analysis. The feature extraction is obtained from 2D methods like the edge detection algorithm. Difficulties appear when signal-to-noise ratios are weak or when more than one vocalization is simultaneously emitted. This is the case...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.395.6641 2023-05-15T17:03:30+02:00 Research Article Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform Olivier Adam The Pennsylvania State University CiteSeerX Archives 2008 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.395.6641 http://asp.eurasipjournals.com/content/pdf/1687-6180-2008-245936.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.395.6641 http://asp.eurasipjournals.com/content/pdf/1687-6180-2008-245936.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://asp.eurasipjournals.com/content/pdf/1687-6180-2008-245936.pdf text 2008 ftciteseerx 2016-01-08T02:27:55Z The study of cetacean vocalizations is usually based on spectrogram analysis. The feature extraction is obtained from 2D methods like the edge detection algorithm. Difficulties appear when signal-to-noise ratios are weak or when more than one vocalization is simultaneously emitted. This is the case for acoustic observations in a natural environment and especially for the killer whales which swim in groups. To resolve this problem, we propose the use of the Hilbert-Huang transform. First, we illustrate how few modes (5) are satisfactory for the analysis of these calls. Then, we detail our approach which consists of combining the modes for extracting the time-varying frequencies of the vocalizations. This combination takes advantage of one of the empirical mode decomposition properties which is that the successive IMFs represent the original data broken down into frequency components from highest to lowest frequency. To evaluate the performance, our method is first applied on the simulated chirp signals. This approach allows us to link one chirp to one mode. Then we apply it on real signals emitted by killer whales. The results confirm that this method is a favorable alternative for the automatic extraction of killer whale vocalizations. Copyright © 2008 Olivier Adam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Text Killer Whale Killer whale Unknown |
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The study of cetacean vocalizations is usually based on spectrogram analysis. The feature extraction is obtained from 2D methods like the edge detection algorithm. Difficulties appear when signal-to-noise ratios are weak or when more than one vocalization is simultaneously emitted. This is the case for acoustic observations in a natural environment and especially for the killer whales which swim in groups. To resolve this problem, we propose the use of the Hilbert-Huang transform. First, we illustrate how few modes (5) are satisfactory for the analysis of these calls. Then, we detail our approach which consists of combining the modes for extracting the time-varying frequencies of the vocalizations. This combination takes advantage of one of the empirical mode decomposition properties which is that the successive IMFs represent the original data broken down into frequency components from highest to lowest frequency. To evaluate the performance, our method is first applied on the simulated chirp signals. This approach allows us to link one chirp to one mode. Then we apply it on real signals emitted by killer whales. The results confirm that this method is a favorable alternative for the automatic extraction of killer whale vocalizations. Copyright © 2008 Olivier Adam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Olivier Adam |
spellingShingle |
Olivier Adam Research Article Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform |
author_facet |
Olivier Adam |
author_sort |
Olivier Adam |
title |
Research Article Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform |
title_short |
Research Article Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform |
title_full |
Research Article Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform |
title_fullStr |
Research Article Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform |
title_full_unstemmed |
Research Article Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform |
title_sort |
research article segmentation of killer whale vocalizations using the hilbert-huang transform |
publishDate |
2008 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.395.6641 http://asp.eurasipjournals.com/content/pdf/1687-6180-2008-245936.pdf |
genre |
Killer Whale Killer whale |
genre_facet |
Killer Whale Killer whale |
op_source |
http://asp.eurasipjournals.com/content/pdf/1687-6180-2008-245936.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.395.6641 http://asp.eurasipjournals.com/content/pdf/1687-6180-2008-245936.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766057396166197248 |