Cross-Domain Underwater Sound Source Localization Algorithm Based on Binaural Matrix and Mutual Information Constraint Loss

Abstract

The accuracy of existing underwater sound source localization algorithms is unsatisfactory, and most of them cannot achieve cross-domain localization. To solve these problems, a cross-domain underwater sound source localization algorithm based on a binaural matrix and mutual information constraint loss is proposed. In this algorithm, a new binaural matrix feature is first extracted based on binaural cues, which is less susceptible to environmental interference and can obtain reliable direction information from received signals. Then, a constrained loss based on mutual information is designed to constrain the proposed neural network to accurately learn the shared representations of different domains. This ensures that the high-dimensional representations used for localization have more explicit orientation directionality. Finally, a cross-domain underwater sound source localization network is constructed to achieve accurate cross-domain localization. Experimental results indicate that the algorithm proposed in this study has a higher localization accuracy than comparative algorithms, both in the same domain and in different domains.