Multispecies bioacoustic classification using transfer learning of deep convolutional neural networks with pseudo-labeling

Abstract In this study, we evaluated deep convolutional neural networks for classifying the calls of 24 birds and amphibian species detected in ambient field recordings from the tropical mountains of Puerto Rico. Training data were collected using a template-based detection algorithm followed by a manual validation process. As preparing sufficient training data is a major challenge for many deep learning applications, we propose a novel approach that combines transfer learning of a pre-trained deep convolutional neural network (CNN) model and a semi-supervised pseudo-labeling method with a custom loss function to meet this challenge. Our proposed methodology enables the network to be trained in a supervised fashion with labeled and unlabeled data simultaneously, which effectively increases the size of training set and thus boosts the model performance. In classifying a test set of manually validated positive and negative template-based detections, our proposed model achieves 97.7% sensitivity (true positive rate), 96.4% specificity (true negative rate) and 99.5% Area Under a Curve (AUC). This multi-label multi-species classification methodology and its framework can be easily adopted by other acoustic classification problems.