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 mel-frequency cepstral coefficients (MFCC) and analyzed with different deep neural network architectures. A deep Transformer 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 Transformer neural networks.
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April 01 2022
Ice anthropogenic classification with acoustic vector sensors using transformer neural networks
Steven Whitaker;
Steven Whitaker
Dept. of Elec. and Comput. Eng., Michigan Technol. Univ., 1600 Townsend Dr., Houghton, MI 49931sjwhitak@mtu.edu
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Andrew Barnard;
Andrew Barnard
Acoust., Penn State, State College, PA
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George D. Anderson;
George D. Anderson
Naval Undersea Warfare Ctr., Newport, RI
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Timothy Havens
Timothy Havens
Dept. of Comput. Sci., Michigan Technol. Univ., Houghton, MI
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J Acoust Soc Am 151, A233 (2022)
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Steven Whitaker, Andrew Barnard, George D. Anderson, Timothy Havens; Ice anthropogenic classification with acoustic vector sensors using transformer neural networks. J Acoust Soc Am 1 April 2022; 151 (4_Supplement): A233. https://doi.org/10.1121/10.0011166
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