Underwater Acoustic Data

Underwater acoustic data analysis focuses on extracting meaningful information from sound recordings in aquatic environments, primarily for ecological monitoring and target recognition. Current research emphasizes developing robust machine learning models, including deep learning architectures like convolutional neural networks and generative adversarial networks, to address challenges such as noise reduction, data scarcity, and the high variability of underwater sounds. These advancements are improving the accuracy of marine mammal vocalization classification, facilitating efficient analysis of large datasets, and enabling applications such as estimating wind speed and quantifying gas release from the seafloor. Ultimately, these improvements enhance our understanding of marine ecosystems and support informed decision-making in areas like environmental protection and resource management.