Underwater Acoustics

This chapter explores the latest advancements and applications of deep learning techniques in the field of underwater acoustics, with a particular focus on the analysis and processing of underwater ship-radiated noise. The chapter begins by introducing the representation of underwater acoustic signals, including traditional spectral analysis techniques such as LOFAR and DEMON, wavelet analysis, etc.. These techniques provide a critical foundation for the preprocessing and feature extraction of underwater acoustic signals. Subsequently, the chapter delves into the application of deep learning in underwater acoustic signal processing. Supervised learning methods, trained on labeled data, are widely used for tasks such as target detection and classification. The core focus of the chapter is on self-supervised learning methods, which leverage unlabeled data to learn effective feature representations, significantly enhancing model performance in data-scarce environments. Additionally, the chapter examines the application of deep learning in specific tasks such as target detection and classification and localization and tracking, demonstrating its potential to improve the accuracy and efficiency of underwater acoustic signal processing. By integrating traditional signal processing techniques with modern deep learning algorithms, this chapter provides a comprehensive understanding of the subject, showcasing the broad application prospects and future directions of deep learning in the field of underwater acoustics.