Reducing ambient noise diffusion model for underwater acoustic target.

Abstract

The recognition of underwater acoustic targets is a challenging problem, and irregular ambient noise is a key factor limiting the effectiveness of the recognition. Research on diffusion models in the audio field has mainly centered around the human voice, and it may be valuable to apply them to the field of underwater acoustic. In this paper, we propose a general method for reducing ambient noise based on the diffusion model. A Decapitation normalization method is proposed, which balances the data distribution of different frequency scales and unifies the noise addition in the time and frequency domains. Then a Reducing Ambient Noise Diffusion (RAND) Model is proposed based on the diffusion model, which can effectively remove the ambient noise in a small range of steps. Considering that some steps of sampling may have a negative effect, a Three-condition mask method is proposed to make the model more robust during sampling. The effectiveness of the proposed method is verified by experiments in the time and frequency domains.