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.

中文翻译：

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一种检测北大西洋露脊鲸上调的多模型深度学习算法

我们提出了一种使用多模型深度学习 (MMDL) 算法检测北大西洋露脊鲸 (NARW) 上行调用的新方法。MMDL 检测器是包含卷积神经网络 (CNN) 和堆叠自动编码器 (SAE) 的分类器，以及用于评估其输出以做出最终决定的融合分类器。MMDL 检测器的目标是分类器选择的多样性，以便学习其架构以适应数据。来自无源声学传感器的信号的频谱图和标度图用于训练 MMDL 检测器。在以前的应用程序的指导下，我们用频谱图训练了 CNN，用标度图训练了 SAE。单个模型的输出由融合分类器评估。将从 MMDL 算法获得的结果与从使用手工特征训练的传统机器学习算法获得的结果进行比较。MMDL算法在上行检测率、非上行检测率和误报率方面均表现出优越性。MMDL 探测器的自主性可立即应用于有效监测和保护世界上最濒危物种之一，其中低密度物种的准确呼叫检测至关重要，尤其是在高声学掩蔽的环境中。