Recognition of imbalanced underwater acoustic datasets with exponentially weighted cross-entropy loss

Abstract

Class imbalance, an objective problem of underwater acoustic datasets, has hardly been paid attention to, but often results in low recognition accuracy of minority classes. The main objective of this paper is to provide an effective method for the recognition of imbalanced underwater acoustic datasets. For this purpose, an exponentially weighted cross-entropy loss is proposed as the convolutional neural network’s loss function, which adds an impact factor to the standard cross-entropy loss according to the prediction probability of each sample. The proposed approach is evaluated on imbalanced underwater acoustic datasets of targets and communication signals. Recognition accuracy has been increased by 6.67% and 13.33% for underwater targets, 25% and 2.5% for underwater simulation communication signals, and 12.5% for underwater experimental communication signals, compared with the cross-entropy loss and the focal loss. Results on simulation data and experimental data show that the proposed approach can obtain higher recognition accuracy than the cross-entropy loss and the focal loss, which provides evidence for its effectiveness.