The Journal of the Acoustical Society of America · 2022 · 29 citations · 24 references
EngineeringComputer ScienceOcean EngineeringMachine LearningOcean AcousticsUnderwater AcousticsAcoustic LocalizationDeep LearningTransfer LearningUnderwater CommunicationUnderwater LocalizationUnderwater Sound PropagationConvolutional Neural NetworksDeep Learning For LocalizationDeep Learning-based LocalizationOne-hot EncodingSound Source LocalizationSource LocalizationUnderwater Acoustic PropagationUnderwater Source Localization
Underwater source localization by deep neural networks (DNNs) is challenging since training these DNNs generally requires a large amount of experimental data and is computationally expensive. In this paper, label distribution-guided transfer learning (LD-TL) for underwater source localization is proposed, where a one-dimensional convolutional neural network (1D-CNN) is pre-trained with the simulation data generated by an underwater acoustic propagation model and then fine-tuned with a very limited amount of experimental data. In particular, the experimental data for fine-tuning the pre-trained 1D-CNN are labeled with label distribution vectors instead of one-hot encoded vectors. Experimental results show that the performance of underwater source localization with a very limited amount of experimental data is significantly improved by the proposed LD-TL.
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