Passive CFAR detection based on continuous wavelet transform of sound signals of marine animal

Wang, Qing; Zhou, Bin; Yu, Wei Miao

doi:10.1109/icspcc.2017.8242460

Cited by 1 publication

(1 citation statement)

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“…The recognition effect of bowhead whale voices hinges on the efficient extraction of target features and the design of classifiers [20]. Currently, traditional feature extraction methods such as the short-time Fourier transform (STFT) [21][22][23][24], wavelet transform (WT) [23,25,26], Winger-Ville distribution (PWVD) [27] and Hilbert-Huang Transform (HHT) [28] are used for bowhead whale voice recognition. All of the above methods have their shortcomings in terms of time-frequency feature extraction, which are summarized below.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Deep Learning Bowhead Whale Whistle Recognizing Method Based on Adaptive SWT: Applying to the Beaufort Sea

Feng,

Xu,

Jin

et al. 2023

Remote Sensing

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The bowhead whale is a vital component of the maritime environment. Using deep learning techniques to recognize bowhead whales accurately and efficiently is crucial for their protection. Marine acoustic remote sensing technology is currently an important method to recognize bowhead whales. Adaptive SWT is used to extract the acoustic features of bowhead whales. The CNN-LSTM deep learning model was constructed to recognize bowhead whale voices. Compared to STFT, the adaptive SWT used in this study raises the SCR for the stationary and nonstationary bowhead whale whistles by 88.20% and 92.05%, respectively. Ten-fold cross-validation yields an average recognition accuracy of 92.85%. The method efficiency of this work was further confirmed by the consistency found in the Beaufort Sea recognition results and the fisheries ecological study. The research results in this paper help promote the application of marine acoustic remote sensing technology and the conservation of bowhead whales.

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