Underwater Acoustic Signal Recognition Based on Salient Feature
This addresses limitations in expert-based systems for underwater acoustic signal recognition, though it appears incremental as it applies existing deep learning techniques to this domain.
This paper tackles underwater acoustic signal recognition in complex environments by proposing a neural network method that learns features from spectra for classification, achieving enhanced performance through automatic feature learning and weight adjustment.
With the rapid advancement of technology, the recognition of underwater acoustic signals in complex environments has become increasingly crucial. Currently, mainstream underwater acoustic signal recognition relies primarily on time-frequency analysis to extract spectral features, finding widespread applications in the field. However, existing recognition methods heavily depend on expert systems, facing limitations such as restricted knowledge bases and challenges in handling complex relationships. These limitations stem from the complexity and maintenance difficulties associated with rules or inference engines. Recognizing the potential advantages of deep learning in handling intricate relationships, this paper proposes a method utilizing neural networks for underwater acoustic signal recognition. The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals. Deep learning models can automatically learn abstract features from data and continually adjust weights during training to enhance classification performance.