  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.

Machine Learning;Artificial Intelligence;Signal Processing