Semi-Supervised Noise Classification Based on Auto-Encoder

Supervised classification algorithms are often used for marine noise classification. However, limited by insufficient labeled samples, the performance of the supervised classification method is typically influenced. To alleviate the limitations of insufficient labeled samples, in this paper, a semi-supervised noise classification method based on an auto-encoder (AE) has been proposed using radiated noise of four kinds of ships. This method takes a two-step training process, including unsupervised pre-training and supervised fine-tuning, making full use of unlabeled data and limited labeled data, respectively, which reduces reliance on label information for noise classification. The performance of this method is compared with traditional backpropagation neural networks (BPNN) and support vector machines (SVM). Experimental data analysis demonstrates that the semi-supervised noise classification method has improved the accuracy with different amounts of labeled samples, especially when labeled samples are relatively rare.