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New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods

Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time–frequency analysis tool for marine mammal vocalizations. In this paper, new MS feature innovation informatio...

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Autores principales: Lin, Chin-Feng, Wu, Bing-Run, Chang, Shun-Hsyung, Parinov, Ivan A., Shevtsov, Sergey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458355/
https://www.ncbi.nlm.nih.gov/pubmed/37631764
http://dx.doi.org/10.3390/s23167228
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author Lin, Chin-Feng
Wu, Bing-Run
Chang, Shun-Hsyung
Parinov, Ivan A.
Shevtsov, Sergey
author_facet Lin, Chin-Feng
Wu, Bing-Run
Chang, Shun-Hsyung
Parinov, Ivan A.
Shevtsov, Sergey
author_sort Lin, Chin-Feng
collection PubMed
description Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time–frequency analysis tool for marine mammal vocalizations. In this paper, new MS feature innovation information of HWV signals was extracted using the EMD analysis method. Thirty-six HWV samples with a time duration of 17.2 ms were classified into Classes I, II, and III, which consisted of 15, 5, and 16 samples, respectively. The following ratios were evaluated: the average energy ratios of the 1 first intrinsic mode function (IMF1) and residual function (RF) to the referred total energy for the Class I samples; the average energy ratios of the IMF1, 2nd IMF (IMF2), and RF to the referred total energy for the Class II samples; the average energy ratios of the IMF1, 6th IMF (IMF6), and RF to the referred total energy for the Class III samples. These average energy ratios were all more than 10%. The average energy ratios of IMF1 to the referred total energy were 9.825%, 13.790%, 4.938%, 3.977%, and 3.32% in the 2980–3725, 3725–4470, 4470–5215, 10,430–11,175, and 11,175–11,920 Hz bands, respectively, in the Class I samples; 14.675% and 4.910% in the 745–1490 and 1490–2235 Hz bands, respectively, in the Class II samples; 12.0640%, 6.8850%, and 4.1040% in the 2980–3725, 3725–4470, and 11,175–11,920 Hz bands, respectively, in the Class III samples. The results of this study provide a better understanding, high resolution, and new innovative views on the information obtained from the MS features of the HWV signals.
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spelling pubmed-104583552023-08-27 New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods Lin, Chin-Feng Wu, Bing-Run Chang, Shun-Hsyung Parinov, Ivan A. Shevtsov, Sergey Sensors (Basel) Article Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time–frequency analysis tool for marine mammal vocalizations. In this paper, new MS feature innovation information of HWV signals was extracted using the EMD analysis method. Thirty-six HWV samples with a time duration of 17.2 ms were classified into Classes I, II, and III, which consisted of 15, 5, and 16 samples, respectively. The following ratios were evaluated: the average energy ratios of the 1 first intrinsic mode function (IMF1) and residual function (RF) to the referred total energy for the Class I samples; the average energy ratios of the IMF1, 2nd IMF (IMF2), and RF to the referred total energy for the Class II samples; the average energy ratios of the IMF1, 6th IMF (IMF6), and RF to the referred total energy for the Class III samples. These average energy ratios were all more than 10%. The average energy ratios of IMF1 to the referred total energy were 9.825%, 13.790%, 4.938%, 3.977%, and 3.32% in the 2980–3725, 3725–4470, 4470–5215, 10,430–11,175, and 11,175–11,920 Hz bands, respectively, in the Class I samples; 14.675% and 4.910% in the 745–1490 and 1490–2235 Hz bands, respectively, in the Class II samples; 12.0640%, 6.8850%, and 4.1040% in the 2980–3725, 3725–4470, and 11,175–11,920 Hz bands, respectively, in the Class III samples. The results of this study provide a better understanding, high resolution, and new innovative views on the information obtained from the MS features of the HWV signals. MDPI 2023-08-17 /pmc/articles/PMC10458355/ /pubmed/37631764 http://dx.doi.org/10.3390/s23167228 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lin, Chin-Feng
Wu, Bing-Run
Chang, Shun-Hsyung
Parinov, Ivan A.
Shevtsov, Sergey
New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods
title New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods
title_full New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods
title_fullStr New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods
title_full_unstemmed New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods
title_short New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods
title_sort new marginal spectrum feature information views of humpback whale vocalization signals using the emd analysis methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458355/
https://www.ncbi.nlm.nih.gov/pubmed/37631764
http://dx.doi.org/10.3390/s23167228
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