A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition

Abstract

The performance of an Unmanned Underwater Vehicle (UUV) is significantly influenced by the magnitude of self-generated noise, making it a crucial factor in advancing acoustic load technologies. Effective noise management, through the identification and separation of various self-noise types, is essential for enhancing a UUV’s reception capabilities. This paper concentrates on the development of UUV self-noise separation techniques, with a particular emphasis on feature extraction and separation in multi-task learning environments. We introduce an enhancement module designed to leverage noise categorization for improved network efficiency. Furthermore, we propose a neural network-based multi-task framework for the identification and separation of self-noise, the efficacy of which is substantiated by experimental trials conducted in a lake setting. The results demonstrate that our network outperforms the Conv-tasnet baseline, achieving a 0.99 dB increase in Signal-to-Interference-plus-Noise Ratio (SINR) and a 0.05 enhancement in the recognized energy ratio.

Keywords

• self-noise
• identification and separation
• sound event recognition
• neural network
• multi-task network