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

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Abstract

We present a new method of detecting North Atlantic Right Whale (NARW) upcalls using a Multimodel Deep Learning (MMDL) algorithm. A MMDL detector is a classifier that embodies Convolutional Neural Networks (CNNs) and Stacked Auto Encoders (SAEs) and a fusion classifier to evaluate their output for a final decision. The MMDL detector aims for diversity in the choice of the classifier so that its architecture is learned to fit the data. Spectrograms and scalograms of signals from passive acoustic sensors are used to train the MMDL detector. Guided by previous applications, we trained CNNs with spectrograms and SAEs with scalograms. Outputs from individual models were evaluated by the fusion classifier. The results obtained from the MMDL algorithm were compared to those obtained from conventional machine learning algorithms trained with handcrafted features. It showed the superiority of the MMDL algorithm in terms of the upcall detection rate, non-upcall detection rate, and false alarm rate. The autonomy of the MMDL detector has immediate application to the effective monitoring and protection of one of the most endangered species in the world where accurate call detection of a low-density species is critical, especially in environments of high acoustic-masking.