In this work, a convolutional neural network based method is proposed to automatically detect odontocetes echolocation clicks by analyzing acoustic data recordings from a passive acoustic monitoring system. The neural network was trained to distinguish between click and non-click clips and was subsequently converted to a full-convolutional network. The performance of the proposed network was evaluated using synthetic data and real audio recordings. The experimental results indicate that the proposed method works stably with echolocation clicks of different species.

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A method based on a convolutional neural network for the automatic classification of odontocete echolocation clicks is presented. The proposed convolutional neural network comprises six layers: three one-dimensional convolutional layers, two fully connected layers, and a softmax classification layer. Rectified linear units were chosen as the activation function for each convolutional layer. The input to the first convolutional layer is the raw time signal of an echolocation click. Species prediction was performed for groups of m clicks, and two strategies for species label prediction were explored: the majority vote and maximum posterior. Two datasets were used to evaluate the classification performance of the proposed algorithm. Experiments showed that the convolutional neural network can model odontocete species from the raw time signal of echolocation clicks. With the increase in m, the classification accuracy of the proposed method improved. The proposed method can be employed in passive acoustic monitoring to classify different delphinid species and facilitate future studies on odontocetes.

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