New Paper: ‘Addressing class imbalance in deep learning for acoustic target classification’

We published our new paper discussing a method to improve the performance of deep convolutional neural networks for acoustic target classification. Our paper addresses the challenge of class imbalance in the sampling of training and validation data, leading to more accurate target classification.