基于 EMD 分析方法的座头鲸发声信号新的边缘谱特征信息视图。

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

机构信息

Department of Electrical Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan.

Department of Microelectronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 81157, Taiwan.

出版信息

Sensors (Basel). 2023 Aug 17;23(16):7228. doi: 10.3390/s23167228.

DOI:10.3390/s23167228

PMID:37631764

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10458355/

Abstract

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.

摘要

驼背鲸发声（HWV）信号的边际谱（MS）特征信息是一个有趣且重要的研究课题。经验模态分解（EMD）是一种强大的海洋哺乳动物发声的时频分析工具。在本文中，使用 EMD 分析方法提取了 HWV 信号的新 MS 特征创新信息。将 36 个时长为 17.2ms 的 HWV 样本分为 I、II 和 III 类，分别包含 15、5 和 16 个样本。评估了以下比例：I 类样本中第 1 个固有模态函数（IMF1）和残差函数（RF）的平均能量比参考总能量；II 类样本中 IMF1、第 2 个 IMF（IMF2）和 RF 的平均能量比参考总能量；III 类样本中 IMF1、第 6 个 IMF（IMF6）和 RF 的平均能量比参考总能量。这些平均能量比均大于 10%。I 类样本中 IMF1 与参考总能量的平均能量比分别为 9.825%、13.790%、4.938%、3.977%和 3.32%，在 2980-3725、3725-4470、4470-5215、10430-11175 和 11175-11920Hz 频段；II 类样本中分别为 14.675%和 4.910%，在 745-1490 和 1490-2235Hz 频段；III 类样本中分别为 12.0640%、6.8850%和 4.1040%，在 2980-3725、3725-4470 和 11175-11920Hz 频段。本研究的结果为更好地理解、高分辨率和新的创新视角提供了驼背鲸发声信号 MS 特征信息。