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