Multispecies bioacoustics 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 and manually validated with a graphical interface. To reduce the labor intensive and time-consuming process of manual validation, as well as to increase the accuracy of species classification with acoustic recordings, we propose a novel approach that combines transfer learning of a pre-trained deep convolutional neural network (CNN) model, a semi-supervised pseudo-labeling method, and a custom training loss function. While generating sufficient training data is a major challenge for many deep learning applications, 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. As a result, the model achieves 97.7% sensitivity, 96.4% specificity and 96.6% accuracy in classifying a test set of manually validated true and false positive template-based detections. This multi-label multi-species classification methodology and its framework can be easily expanded to other acoustic classification problems.