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

Birdy Phathanapirom (), Jason Hite, Kenneth Dayman, David Chichester and Jared Johnson
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Birdy Phathanapirom: Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Jason Hite: Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Kenneth Dayman: Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
David Chichester: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Jared Johnson: Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA

Data, 2023, vol. 8, issue 4, 1-18

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.

Keywords: data fusion; self-supervised; semi-supervised; classification; infrasound (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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