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

Abstract

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

References in corpus (3)

• Source localization in an ocean waveguide using supervised machine learning
• Convolutional Neural Networks for Passive Monitoring of a Shallow Water Environment using a Single Sensor
• Sound source ranging using a feed-forward neural network with fitting-based early stopping

Cited by in corpus (4)

• Machine learning in acoustics: theory and applications
• Sound source ranging using a feed-forward neural network with fitting-based early stopping
• Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations
• Complex data labeling with deep learning methods: Lessons from fisheries acoustics