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A multimodel deep learning algorithm to detect North Atlantic right whale up-calls.

Ali K Ibrahim¹, Hanqi Zhuang², Laurent M Chérubin¹

¹Harbor Branch Oceanographic Institute, Florida Atlantic University, 5600 US1 North, Fort Pierce, Florida 34946, USA.

The Journal of the Acoustical Society of America

|September 2, 2021

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A new Multimodel Deep Learning (MMDL) algorithm accurately detects North Atlantic Right Whale (NARW) upcalls. This advanced method improves detection rates and reduces false alarms, aiding conservation efforts for this endangered species.

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Area of Science:

Marine Biology
Bioacoustics
Artificial Intelligence

Background:

North Atlantic Right Whales (NARW) are critically endangered.
Effective monitoring is crucial for NARW conservation.
Acoustic masking poses challenges for traditional detection methods.

Purpose of the Study:

To develop a novel algorithm for detecting NARW upcalls.
To improve the accuracy and efficiency of NARW acoustic monitoring.
To leverage deep learning for enhanced species detection.

Main Methods:

Utilized a Multimodel Deep Learning (MMDL) algorithm combining Convolutional Neural Networks (CNNs) and Stacked Auto Encoders (SAEs).
Trained models using spectrograms (CNNs) and scalograms (SAEs) from passive acoustic sensor data.
Employed a fusion classifier to integrate and evaluate individual model outputs.

Main Results:

The MMDL algorithm demonstrated superior performance compared to conventional machine learning methods.
Achieved higher upcall detection rates and non-upcall detection rates.
Significantly reduced the false alarm rate in acoustic monitoring.

Conclusions:

The MMDL detector offers an autonomous and accurate solution for NARW upcall detection.
This method is vital for the effective monitoring and protection of endangered marine species.
The algorithm's robustness is critical in high acoustic-masking environments.