A Synchronous Processing Method for Underwater Sound Source Recognition and Range Estimation Based on Multi-Task Learning and Transfer Learning

This study focuses on underwater sound sources and investigates the application of multi-task learning (MTL) in the synchronous processing of target recognition and range estimation to achieve systematic detection. To enhance the applicability of the MTL model with limited underwater acoustic data, this study integrates transfer learning (TL) with MTL, forming the MTL-TL model. The proposed approach extracts input features of underwater acoustic targets, constructs an MTL model, pre-trains it using a large dataset from a pre-selected marine area, retrains it with a small dataset from the detected sea area based on TL, and performs target recognition and range estimation on test sets. The effectiveness of the proposed MTL-TL method is validated using data from the S5 voyage of SWellEX-96 experiment and is compared with three models: single-task learning (STL), STL-TL, and MTL. The results indicate that the combination of the regularization effect between subtasks of MTL and the empirical information advantage of TL enables the MTL-TL model to achieve accurate target recognition and range estimation, outperforming other models. Furthermore, the model remains effective in broadband target scenarios and cases involving two sound sources of the same frequency. Even at low signal-to-noise ratios, the model remains applicable, achieving the recognition accuracy above 79% and mean absolute percentage error below 8% at 0 dB.