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

**Authors:** Bryan Sagredo, Sonia Español-Jiménez, Felipe Tobar  
**arXiv:** [2110.02151](https://arxiv.org/abs/2110.02151)  
**Date:** 2021-10-05  
**Summary page:** https://www.summarizepaper.com/fr/paper/2110.02151v1/

## 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.