Detection of blue whale vocalisations using a temporal-domain convolutional neural network

TL;DR

This paper introduces a neural network-based framework for detecting blue whale vocalisations in submarine recordings, combining preprocessing, label propagation, and CNN classification, achieving high accuracy and recall on real-world data.

Contribution

The study presents a novel three-stage detection framework that effectively identifies whale calls in noisy underwater recordings, demonstrating promising results with real data.

Findings

Accuracy of 85.4% achieved

Recall of 93.5% achieved

Effective detection even for unlabelled call parts

Abstract

We present a framework for detecting blue whale vocalisations from acoustic submarine recordings. The proposed methodology comprises three stages: i) a preprocessing step where the audio recordings are conditioned through normalisation, filtering, and denoising; ii) a label-propagation mechanism to ensure the consistency of the annotations of the whale vocalisations, and iii) a convolutional neural network that receives audio samples. Based on 34 real-world submarine recordings (28 for training and 6 for testing) we obtained promising performance indicators including an Accuracy of 85.4\% and a Recall of 93.5\%. Furthermore, even for the cases where our detector did not match the ground-truth labels, a visual inspection validates the ability of our approach to detect possible parts of whale calls unlabelled as such due to not being complete calls.