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Convolutional neural network for detecting odontocete echolocation clicks.

Wenyu Luo¹, Wuyi Yang¹, Yu Zhang¹

¹Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, College of Ocean and Earth Science, Xiamen University, Xiamen, Chinaluowenyu@stu.xmu.edu.cn, wyyang@xmu.edu.cn, yuzhang@xmu.edu.cn.

The Journal of the Acoustical Society of America

|February 4, 2019

Summary

This summary is machine-generated.

A new convolutional neural network method automatically detects odontocete echolocation clicks from acoustic data. This stable method accurately identifies clicks from various species in passive acoustic monitoring systems.

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Marine bioacoustics
Artificial intelligence in ecology

Background:

Passive acoustic monitoring (PAM) systems are crucial for marine mammal research.
Automated detection of odontocete echolocation clicks is challenging but essential for large-scale data analysis.

Purpose of the Study:

To develop and evaluate an automated method for detecting odontocete echolocation clicks using convolutional neural networks (CNNs).
To assess the performance of the CNN-based method on both synthetic and real-world acoustic data.

Main Methods:

A CNN was designed and trained to differentiate between echolocation click and non-click segments in acoustic recordings.
The trained CNN was converted into a full-convolutional network for enhanced processing.
Performance evaluation involved testing with simulated datasets and field-recorded audio data.

Main Results:

The proposed CNN-based method demonstrated stable and accurate detection of odontocete echolocation clicks.
The network effectively distinguished clicks from non-click sounds in diverse acoustic environments.
Successful validation was achieved using both synthetic and real-world audio recordings.

Conclusions:

Convolutional neural networks offer a robust solution for automated odontocete echolocation click detection.
The developed method shows promise for improving the efficiency and accuracy of passive acoustic monitoring data analysis.
This approach is applicable to echolocation clicks from multiple odontocete species.