GAN-based Sample Expansion for Underwater Acoustic Signal

Abstract

A deep learning model has a large number of free parameters, which need to be effectively trained on a large number of samples to calculate the depth parameters. However, many special applications like underwater acoustic signal recongnition cannot provide enough dataset to guarantee high performance. In addition, the original dataset adopts some formats, such as audio, which makes it difficult to capture features. To overcome these challenges, we propose a novel framework. Firstly, based on the evaluation of spectrum method, our framework selects the appropriate preprocessing method. Then it modifies GAN to generate samples, and establishes an independent classification network to ensure the quality of samples. Finally, our framework applies the existing classification network to evaluate performance and selects the best one for the LOFAR spectrum. The experimental results demonstrate that the proposed method can generate high-quality LOFAR spectrum and improve the prediction accuracy of the classification model significantly.