Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases

• URL: http://arxiv.org/abs/2308.01138v2
• Date: Mon, 14 Aug 2023 12:37:37 GMT
• Title: Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases
• Authors: Haiwen Du, Zheng Ju, Yu An, Honghui Du, Dongjie Zhu, Zhaoshuo Tian, Aonghus Lawlor, Ruihai Dong
• Abstract summary: spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. We propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns. We generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning.
• Score: 10.876490928902838
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples. Unfortunately, the inevitable sample-level baseline noise makes the model unable to obtain the paired data that only differ in dataset-level environmental noise. To address the problem, we generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning, enhancing the system's learning performance. Experiments on spectral data with different background noises demonstrate the good noise-transferring ability of the proposed method against baseline systems ranging from wavelet denoising, deep neural networks, and generative models. From this research, we posit that our method can enhance the performance of DL models by generating high-quality cases. The source code is made publicly available online at https://github.com/Magnomic/CNST.

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