Acoustic classifiers are a necessary component in understanding the source. When a foreign object has been classified, physics models can be associated with the foreign object for better localization and tracking. In highly non-linear environments, like shallow ice environments, traditional classifiers cannot properly consider its compounded non-linearities: multi-path, reflective surfaces, scattering fields, and the dynamic acoustic properties of first-year ice. With such significantly distorted signals, we deploy deep neural networks to better classify different acoustic sources. We collected data from 8 different acoustic sources on the Keweenaw Waterway in Houghton, Michigan: a narrow and shallow channel covered with first-year ice. Two sources were moving and the other five were stationary; the sources did not emit simultaneously. Data were recorded using two spatially separated underwater acoustic vector sensors; their time-series data were post-processed into short-time Fourier transform (STFT) bins and analyzed with different deep neural network architectures. A deep Vision Transformer neural network, a Long Short-Term Memory (LSTM) neural network and a deep residual neural network were then compared in their ability to predict which source was emitting. Preliminary results show success with the deep Vision Transformer neural networks.

This content is only available via PDF.