We apply the concept of differentiable simulation to enhance machine learning for passive sonar. Our approach uses a differentiable ambient noise model that can be used as a neural network layer, allowing it to learn optimal data augmentations through interpretable, tunable parameters during training. We use this augmentation layer to adversarially train a neural network that detects acoustic emissions from a merchant ship under varying signal-to-noise ratios (SNRs) and weather conditions. Our adversarial training shows a remarkable ability to generalize performance to low SNR signals as compared to non-adversarial baseline training.

Adversarial Machine Learning Training for Signal-to-Noise Generalization in Passive Undersea Acoustics

We demonstrate an end-to-end neural network approach for acoustic source detection and classification on a linear sensor array. We show that the neural network learns optimal spatial and frequency filters as it performs beamforming-based signal enhancement and finite impulse response (FIR) filtering to isolate source-specific energy signatures. The trained network shows robust performance as a function of signal-to-noise ratio.

An End-to-end Neural Network for Acoustic Source Detection and Classification on a Linear Sensor Array

We propose MagNet, a simple and interpretable time-domain neural network for magnetic anomaly detection. We describe the results of a supervised learning application in which we train MagNet to identify the presence of magnetic dipoles as they pass by a magnetic vector gradiometer. The detection statistics show robust performance as a function of signal-to-noise ratio and the ability to provide detections before the closest point of approach. In addition, we use the interpretable layers of MagNet as a diagnostic tool to provide new insights into sensor data and bulk dynamics of the dipole.

Gregory Byrne

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