Multispecies Bioacoustic Classification using transfer learning of deep convolutional
neural networks with pseudo-labeling
neural networks with pseudo-labeling
In this study, we evaluated deep convolutional neural networks for classifying the calls of 24 birds and amphibian species detected in ambient field recordings. 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. Please email us at contact@rfcx.org for a copy of this article.