What question did this study set out to answer?

The research aims to enhance the detection accuracy of harbor porpoise click trains in noisy habitats using a machine learning model.

January 25, 2026The Journal of the Acoustical Society of AmericaOpen Access

Harbor porpoises and the machine: Assisting manual validation of click trains recorded by Cetacean Porpoise Detector

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Authors

MGMarco F. W. GaugerFederal Institute of Hydrology TTThomas TauppFederal Institute of Hydrology

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Implication

Demonstrates improved accuracy of click train detection in harbor porpoises using machine learning tools, indicating enhanced monitoring capability in noisy environments.

Key Points

The research aims to enhance the detection accuracy of harbor porpoise click trains in noisy habitats using a machine learning model.
Utilized C-PODs for passive acoustic monitoring of harbor porpoises.
Validated a subsample of click trains identified by the KERNO classifier.
Trained an extreme gradient boosting model on physical characteristics of click trains.
Evaluated model performance on both testing and monitoring data.
ML model achieved an accuracy of 0.985 on testing data and 0.849 on monitoring data.
The model showed good generalization beyond the training data.
Reduced risk of missing low-precision estimates from random manual validation.

Cite This Study

Gauger et al. (2026) studied this question.

synapsesocial.com/papers/6975b32bfeba4585c2d6e9cf https://doi.org/10.1121/10.0042220

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Implication

Demonstrates improved accuracy of click train detection in harbor porpoises using machine learning tools, indicating enhanced monitoring capability in noisy environments.

Harbor porpoises and the machine: Assisting manual validation of click trains recorded by Cetacean Porpoise Detector

Discussion

Member takes

Implication

Key Points

Cite This Study

Harbor porpoises and the machine: Assisting manual validation of click trains recorded by Cetacean Porpoise Detector

Discussion

Member takes

Implication

Key Points

Cite This Study

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