Abstract
In recent years, Taiwan’s government has focused on policies regarding offshore wind farming near the Indo-Pacific humpback dolphin habitat, where marine mammal observation is a critical consideration. The present research developed an algorithm called National Taiwan University Passive Acoustic Monitoring (NTU_PAM) to assist marine mammal observers (MMOs). The algorithm performs whistle detection processing and whistle localization. Whistle detection processing is based on image processing and whistle feature extraction; whistle localization is based on the time difference of arrival (TDOA) method. To test the whistle detection performance, we used the same data to compare NTU_PAM and the widely used software PAMGuard. To test whistle localization, we designed a real field experiment where a sound source projected simulated whistles, which were then recorded by several hydrophone stations. The data were analyzed to locate the moving path of the source. The results show that localization accuracy was higher when the sound source position was in the detection region composed of hydrophone stations. This paper provides a method for MMOs to conveniently observe the migration path and population dynamics of cetaceans without ecological disturbance.
Highlights
Most of Taiwan’s raw materials for energy production, including coking coal, fuel coal, crude, and liquefied natural gas [1], are imported and have a large and immediate impact on the environment
The traditional method to detect cetaceans is visual, whereby marine mammal Observers (MMOs) work from vehicles, using the naked eye to search for cetaceans, an operation that is expensive and offers only a low probability of success; it is limited to daylight hours
Underwater acoustics provide an alternative technique to detect marine mammals, and the cetacean call can be used as a specific characteristic of detection
Summary
Most of Taiwan’s raw materials for energy production, including coking coal, fuel coal, crude, and liquefied natural gas [1], are imported and have a large and immediate impact on the environment. The above research is based on supervised machine learning methods requiring numerous sets of clean training data, manually labeling the calls, and building the model. These models are only suitable for specific or regional species. Gillespie’s and Lin’s methods include four main steps: (1) spectrogram, (2) image processing, (3) whistle feature extraction, and (4) combination of the whistle data points. We compared NTU_PAM and PAMGuard, which is regarded as a standard of whistle detection. Tracking cetaceans is another recent primary research subject. A=ries21at0hvnee∑1=r9o0arSgitge−dinn, taf ol build a new spectrogram; the formula spectrogram and , is the(n2)ew spec-
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