North Atlantic Right Whales Up-call Detection Using Multimodel Deep Learning

TL;DR

This paper introduces a multimodel deep learning approach combining CNNs and SAEs for detecting North Atlantic Right Whale up-calls from acoustic data, outperforming traditional methods.

Contribution

The novel integration of CNNs and SAEs with a fusion mechanism for whale call detection improves accuracy over conventional machine learning techniques.

Findings

MMDL achieves higher detection rates.

MMDL reduces false alarms.

CNNs excel with spectrograms, SAEs with scalograms.

Abstract

A new method for North Atlantic Right Whales (NARW) up-call detection using Multimodel Deep Learning (MMDL) is presented in this paper. In this approach, signals from passive acoustic sensors are first converted to spectrogram and scalogram images, which are time-frequency representations of the signals. These images are in turn used to train an MMDL detec-tor, consisting of Convolutional Neural Networks (CNNs) and Stacked Auto Encoders (SAEs). Our experimental studies revealed that CNNs work better with spectrograms and SAEs with sca-lograms. Therefore in our experimental design, the CNNs are trained by using spectrogram im-ages, and the SAEs are trained by using scalogram images. A fusion mechanism is used to fuse the results from individual neural networks. In this paper, the results obtained from the MMDL detector are compared with those obtained from conventional machine learning algorithms trained with handcraft features. It is shown that the performance of the MMDL detector is sig-nificantly better than those of the representative conventional machine learning methods in terms of up-call detection rate, non-up-call detection rate, and false alarm rate.