High-Resolution Time-Frequency Feature Enhancement of Bowhead Whale Calls Based on Local Maximum Synchronous Extraction of Generalized S-Transforms

Abstract

Bowhead whales (Balaena mysticetus) are an important species in the Arctic ecosystem, but their conservation is challenged by environmental noise from shipping and climate change. Effective monitoring of Bowhead whale vocalizations is essential for their conservation, yet traditional acoustic methods face limitations in detecting weak and non-stationary signals amidst complex background noise. In this study, we propose a novel method, Local Maximum Simultaneous Extraction of Generalized S-Transforms (LMSEGST), to enhance the feature extraction of Bowhead whale calls. The LMSEGST method integrates generalized S-transforms with local maximum extraction, improving time-frequency resolution and noise immunity. We compare the performance of LMSEGST with traditional methods (STFT, GST, and LMSST) using synthetic and real-world datasets. The results show that LMSEGST outperforms the other methods, with a 28.32% reduction in Rayleigh entropy compared to STFT at 5 dB SNR and a 28.24% reduction compared to LMSST at 10 dB SNR. Additionally, LMSEGST maintained higher SNR values, demonstrating superior noise resistance. These findings suggest that LMSEGST offers a more robust solution for acoustic monitoring of Bowhead whales, particularly in noisy, Arctic environments, contributing to more effective conservation strategies for this species.

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