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

TL;DR

This paper introduces a frequency-based noise attenuation method that enhances driving event extraction and improves anomaly detection accuracy in tire friction noise analysis, especially under noisy outdoor conditions.

Contribution

The study presents a novel frequency filtering technique that effectively reduces noise and boosts anomaly detection performance in tire friction-based systems.

Findings

Improved anomaly detection accuracy by an average of 8.506%.

Enhanced precision in driving event extraction under noisy conditions.

Practical solution for outdoor edge computing environments.

Abstract

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.