Underwater-Art: Expanding Information Perspectives With Text Templates For Underwater Acoustic Target Recognition

arXiv:2305.19612v2 Announce Type: replace-cross
Abstract: Underwater acoustic target recognition is an intractable task due to the complex acoustic source characteristics and sound propagation patterns. Limited by insufficient data and narrow information perspective, recognition models based on deep learning seem far from satisfactory in practical underwater scenarios. Although underwater acoustic signals are severely influenced by distance, channel depth, or other factors, annotations of relevant information are often non-uniform, incomplete, and hard to use. In our work, we propose to implement Underwater …

abstract art arxiv cs.lg cs.sd data deep learning eess.as information narrow patterns perspective perspectives practical propagation recognition sound text type underwater