Acoustic Explosion Data Archive for Machine Learning

Takazawa, Samuel Kei; Garcés, Milton; Hix, Jay D; Watson, Scott M; Zeiler, Cleat; Kim, Keehoon; Chichester, David L; Ocampo Giraldo, Luis A

DOE OSTI · 3021226

Acoustic Explosion Data Archive for Machine Learning

Takazawa, Samuel Kei·Garcés, Milton·Hix, Jay D·Watson, Scott M·Zeiler, Cleat·Kim, Keehoon·Chichester, David L·Ocampo Giraldo, Luis A

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Abstract

The prompt detection of explosions is a key element of the nuclear non-proliferation mission. With traditional sensors being limited in number and scale, smartphones as compact and economical multi-modal sensors are gaining traction and are being deployed. To address the flood of heterogeneous smartphone data, our team proposes a feature extraction using standardized constant-Q frequency bands across acoustic, barometric, and accelerometer data. The work in this presentation contains data collected at Idaho National Laboratory during planned detonations.

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Takazawa, Samuel Kei, Garcés, Milton, Hix, Jay D, Watson, Scott M, Zeiler, Cleat, Kim, Keehoon, Chichester, David L, Ocampo Giraldo, Luis A. 2020-02-08. Acoustic Explosion Data Archive for Machine Learning. https://www.osti.gov/biblio/3021226

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