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

arXiv: https://arxiv.org/abs/2210.11068 · HTML: https://mlsanity.com/paper/2210.11068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [A Wavenet for Speech Denoising](https://mlsanity.com/paper/1706.07162/) (2017)
- [Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection](https://mlsanity.com/paper/2111.10985/) (2021)
- [Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection](https://mlsanity.com/paper/2112.07214/) (2021)

## Built on by (most cited first, 1 of 1)

- [Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection](https://mlsanity.com/paper/2307.04298/) (2023)

## Abstract (arXiv)

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.