Underwater Acoustic Signal

Underwater acoustic signal processing aims to extract meaningful information from sound waves propagating underwater, crucial for applications like communication, target recognition, and environmental monitoring. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long Short-Term Memory (LSTM) networks, often combined with advanced signal processing techniques for denoising, feature extraction, and source separation. These advancements address challenges posed by the complex underwater acoustic environment, improving the accuracy and robustness of underwater systems for various scientific and engineering purposes. The ultimate goal is to enhance the reliability and efficiency of underwater operations across diverse fields.