Ship Radiated Noise

Ship-radiated noise analysis focuses on identifying and characterizing the sounds produced by vessels in the ocean, primarily for underwater target recognition and environmental monitoring. Current research emphasizes developing robust deep learning models, such as convolutional neural networks, often incorporating techniques like continual learning and adaptive wavelet transforms, to improve accuracy in challenging acoustic environments with variable background noise and transmission conditions. These advancements are crucial for enhancing maritime security, optimizing vessel traffic management, and assessing the impact of shipping noise on marine ecosystems. Data augmentation and pruning strategies are also being explored to address the limitations of scarce and noisy underwater acoustic datasets.