Underwater acoustic classification across diverse sea environments using adaptive regularization and knowledge encoding

Underwater acoustic classification plays a critical role in both commercial and military applications. It faces significant challenges due to high background noise and the dynamic nature of marine environments. Factors such as temperature, pressure, and salinity create complex sound propagation patterns, causing feature space variations. These variations cause degraded performance of classifications systems when deployed in unseen conditions. Traditional approaches often rely on isolated learning paradigms, limiting their adaptability across diverse sea environments. To overcome this, we propose a novel learning strategy that dynamically extracts, retains, and transfers knowledge to improve generalization. Our method introduces an adaptive regularization technique for stable weight and learning rate updates. In addition, a robust knowledge encoding scheme is introduced to preserve and reuse learned features effectively. To demonstrate the effectiveness of the proposed method, extensive experiments are conducted on benchmark underwater acoustic datasets collected from various environments. The experimental results show that the proposed method achieves superior performance in diverse sea environments.