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Block-Adaptive Rényi Entropy-Based Denoising for Non-Stationary Signals
Nicoletta Saulig1, Jonatan Lerga2,3, Siniša Miličić4
1Faculty of Engineering, Juraj Dobrila University of Pula, 52100 Pula, Croatia.
Sensors (Basel, Switzerland)
|November 11, 2022
Summary
This study introduces an adaptive signal denoising method for time-variable noise. It effectively recovers useful signal information by analyzing short time blocks, outperforming global methods.
Area of Science:
- Signal Processing
- Information Theory
- Data Analysis
Background:
- Non-stationary noise causes time-varying signal degradation.
- Accurate signal information recovery is challenging under these conditions.
- Existing global criteria may not effectively handle temporal noise variations.
Purpose of the Study:
- To develop an adaptive signal denoising method for time-variable noise environments.
- To improve the recovery of useful signal components.
- To address the limitations of global denoising criteria.
Main Methods:
- Proposes a denoising method based on amplitude segmentation.
- Utilizes local Rényi entropy estimation over short signal spectrogram blocks.
- Transforms the denoising problem into a stationary noise case through local feature estimation.
Main Results:
- Demonstrates superior performance on both synthetic and real-world data.
- The proposed adaptive method consistently outperforms denoising based on global criteria.
- Local feature estimation effectively handles non-stationary noise characteristics.
Conclusions:
- The proposed adaptive denoising method is effective for time-variable noise.
- Local analysis of signal features enhances denoising performance.
- This approach offers a significant improvement over traditional global methods.
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