Extraction of Energy Characteristics of Blue Whale Vocalizations Based on Empirical Mode Decomposition

• Published in: Sensors
• Main Authors: Wen, Chai-Sheng, Lin, Chin-Feng, Chang, Shun-Hsyung
• Format: Text
• Language: English
• Published: MDPI 2022
• Subjects: Article; Scripps; Blue whale
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002675/; http://www.ncbi.nlm.nih.gov/pubmed/35408351; https://doi.org/10.3390/s22072737

This study extracts the energy characteristic distributions of the intrinsic mode functions (IMFs) and residue functions (RF) for a blue whale sound signal, with empirical mode decomposition (EMD) as the basic theoretical framework. A high-resolution marginal frequency characteristics extraction method, based on EMD with energy density intensity (EDI) parameters for blue B call vocalizations, was proposed. The extraction algorithm included six steps: EMD, energy analysis, marginal frequency (MF) analysis with EDI parameters, feature extraction (FE), classification, and Hilbert spectrum (HS) analysis. The blue whale sound sources were obtained from the website of the Scripps Whale Acoustics Lab of the University of California, San Diego, USA. The source is a type of B call with a time duration of 46.65 s, from which 59 analysis samples with a time duration of 180 ms were taken. The average energy distribution ratios of the IMF1, IMF2, IMF3, IMF4, and RF are 49.06%, 20.58%, 13.51%, 10.94% and 3.84%, respectively. New classification criteria and EDI parameters were proposed to extract the blue whale B call vocalization (BWBCV) characteristics. The analysis results show that the main frequency bands of the signal are distributed at 41–43 Hz in the MF of IMF1 for Class I BWBCV and 11–13 Hz in the MF of IMF2 for Class II BWBCV, respectively.