Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure

Birdy Phathanapirom, Jason Hite, Kenneth Dayman, David Chichester, Jared Johnson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defined or constrained, manually developing a training dataset can often overlook interesting phenomena that should be included. To this end, we have developed and demonstrated an iterative self-supervised learning procedure, whereby models are successfully trained and applied to new data to extract new training examples that are added to the corpus of training data. Successive generations of classifiers are then trained on this augmented corpus. Using low-frequency acoustic data collected by a network of infrasound sensors deployed around the High Flux Isotope Reactor and Radiochemical Engineering Development Center at Oak Ridge National Laboratory, we test the viability of our proposed approach to develop a powerful classifier with the goal of identifying vehicles from continuously streamed data and differentiating these from other sources of noise such as tools, people, airplanes, and wind. Using a small collection of exhaustively manually labeled data, we test several implementation details of the procedure and demonstrate its success regardless of the fidelity of the initial model used to seed the iterative procedure. Finally, we demonstrate the method’s ability to update a model to accommodate changes in the data-generating distribution encountered during long-term persistent data collection.

Original languageEnglish
Article number64
JournalData
Volume8
Issue number4
DOIs
StatePublished - Apr 2023

Funding

This work was funded by the Office of Defense Nuclear Nonproliferation Research and Development (NA-22), within the US Department of Energy’s (DOE) National Nuclear Security Administration. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US DOE. By accepting the article for publication, the US government along with the publisher acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or re-produce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

FundersFunder number
DOE Public Access Plan
Office of Defense Nuclear Nonproliferation Research and DevelopmentNA-22
U.S. Department of Energy
National Nuclear Security AdministrationDE-AC05-00OR22725

    Keywords

    • classification
    • data fusion
    • infrasound
    • self-supervised
    • semi-supervised

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