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Accurately extracting driving events is the way to maximize computationalefficiency and anomaly detection performance in the tire frictional nose-basedanomaly detection task. This study proposes a concise and highly useful methodfor improving the precision of the event extraction that is hindered by extranoise such as wind noise, which is difficult to characterize clearly due to itsrandomness. The core of the proposed method is based on the identification ofthe road friction sound corresponding to the frequency of interest and removingthe opposite characteristics with several frequency filters. Our method enablesprecision maximization of driving event extraction while improving anomalydetection performance by an average of 8.506%. Therefore, we conclude ourmethod is a practical solution suitable for road surface anomaly detectionpurposes in outdoor edge computing environments.

Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance