Work

Sound source ranging using a feed-forward neural network trained with fitting-based early stopping

Year: 2019

Type: article

Abstract: When a feed-forward neural network (FNN) is trained for acoustic source ranging in an ocean waveguide, it is difficult evaluating the FNN ranging accuracy of unlabeled test data. The label is the distance between source and receiver array. A fitting-based early stopping (FEAST) method is introduced to evaluate the FNN ranging error on test data where the distance to the source is unknown. Based on FEAST, when the evaluated ranging error is minimum on test data, training is stopped. This will improve the FNN ranging accuracy on the test data. The FEAST is demonstrated on simulated and experimental data.

Source: The Journal of the Acoustical Society of America

DOI: 10.1121/1.5126115

Authors: Jing Chi, Xiaolei Li, Haozhong Wang, Dazhi Gao, Peter Gerstoft

Institutions: Ocean University of China, Scripps Institution of Oceanography, University of California San Diego

Language: English

Topic: Underwater Acoustics Research

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Qingdao National Laboratory for Marine Science and Technology

Awards: G1722632777, G2333581167, G4454716093, G7684968424