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Rocket Launch Detection with Smartphone Audio and Transfer Learning

Journal Article · · Signals
 [1];  [2];  [1]
  1. University of Hawai’i at Mānoa, Honolulu, HI (United States)
  2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
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Rocket launches generate infrasound signatures that have been detected at great distances. Due to the sparsity of the networks that have made these detections, however, most signals are detected tens of minutes to hours after the rocket launch. In this work, a method of near-real-time detection of rocket launches using data from a network of smartphones located 10–70 km from launch sites is presented. A machine learning model is trained and tested on the open-access Aggregated Smartphone Timeseries of Rocket-generated Acoustics (ASTRA), Smartphone High-explosive Audio Recordings Dataset (SHAReD), and ESC-50 datasets, resulting in a final accuracy of 97% and a false positive rate of <1%. The performance and behavior of the model are summarized, and its suitability for persistent monitoring applications is discussed.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
2585361
Report Number(s):
LLNL--JRNL-2006716
Journal Information:
Signals, Journal Name: Signals Journal Issue: 3 Vol. 6; ISSN 2624-6120
Publisher:
MDPI AGCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

acoustics
data
detection
infrasound
machine learning
rocket launches
smartphones