Locally Self-Adjustive Smoothing for Measurement Noise Reduction with Application to Automated Peak Detection

2023/10/23 by Keisuke Ozawa, Ozawa, Keisuke, Tomoya Itakura +3 · 1 citation
Computer Science · Engineering · #FOS: Electrical engineering #FOS: Physical sciences #Fault Detection and Control Systems #Image and Signal Denoising Methods #Optics (physics.optics) #Signal Processing (eess.SP) #Structural Health Monitoring Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2310.17663

openalex publication_date 2023/10/23 · openalex created_date 2023/11/01 · openalex updated_date 2026/07/28

Abstract

Smoothing is widely used approach for measurement noise reduction in spectral analysis. However, it suffers from signal distortion caused by peak suppression. A locally self-adjustive smoothing method is developed that retains sharp peaks and less distort signals. The proposed method uses only one parameter that determines global smoothness, while balancing the local smoothness using data itself. Simulation and real experiments in comparison with existing convolution-based smoothing methods indicate both qualitatively and quantitatively improved noise reduction performance in practical scenarios. We also discuss parameter selection and demonstrate an application for the automated smoothing and detection of a given number of peaks from noisy measurement data.

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