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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdessamad, E., Saadane, R., El Aroussi, M., Wahbi, M., Hamdoun, A.: Spectrum sensing with an improved energy detection. In: 2014 International Conference on Multimedia Computing and Systems (ICMCS), pp. 895–900. IEEE (2014)
An, Y., et al.: Current state and future directions for deep learning based automatic seismic fault interpretation: a systematic review. Earth Sci. Rev. 243, 104509 (2023)
Bukin, O., et al.: New solutions of laser-induced fluorescence for oil pollution monitoring at sea. Photonics 7, 36 (2020)
Chandra, B., Sharma, R.K.: Adaptive noise schedule for denoising autoencoder. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8834, pp. 535–542. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12637-1_67
Chen, S., et al.: Olive oil classification with laser-induced fluorescence (LIF) spectra using 1-dimensional convolutional neural network and dual convolution structure model. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 279, 121418 (2022)
Du, H.: Laser-induced fluorescence spectral data of humic acid solution in different noise patterns (2023). https://doi.org/10.21227/7r0c-mf67
Du, H., et al.: Disentangling noise patterns from seismic images: noise reduction and style transfer. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2022)
He, W., Zi, Y., Chen, B., Wang, S., He, Z.: Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis. Sci. China Technol. Sci. 56, 1956–1965 (2013)
Hu, F., et al.: Identification of mine water inrush using laser-induced fluorescence spectroscopy combined with one-dimensional convolutional neural network. RSC Adv. 9(14), 7673–7679 (2019)
Hu, F., et al.: Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 219, 367–374 (2019)
Kaneko, T., Kameoka, H., Hiramatsu, K., Kashino, K.: Sequence-to-sequence voice conversion with similarity metric learned using generative adversarial networks. In: Interspeech, vol. 2017, pp. 1283–1287 (2017)
Kaneko, T., Kameoka, H., Tanaka, K., Hojo, N.: CycleGAN-VC2: improved CycleGAN-based non-parallel voice conversion. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2019, pp. 6820–6824. IEEE (2019)
Kazemzadeh, M., Hisey, C.L., Zargar-Shoshtari, K., Xu, W., Broderick, N.G.: Deep convolutional neural networks as a unified solution for Raman spectroscopy-based classification in biomedical applications. Optics Commun. 510, 127977 (2022)
Kenny, E.M., Keane, M.T.: On generating plausible counterfactual and semi-factual explanations for deep learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11575–11585 (2021)
Kwon, Y.H., Park, M.G.: Predicting future frames using retrospective cycle GAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1811–1820 (2019)
Laurent, G., Woelffel, W., Barret-Vivin, V., Gouillart, E., Bonhomme, C.: Denoising applied to spectroscopies-part i: concept and limits. Appl. Spectrosc. Rev. 54(7), 602–630 (2019)
Liu, Z., Wu, H., Du, H., Luo, Z., Tang, M.: Distributed temperature and curvature sensing based on Raman scattering in few-mode fiber. IEEE Sens. J. 22(23), 22620–22626 (2022)
Loh, W., et al.: Operation of an optical atomic clock with a Brillouin laser subsystem. Nature 588(7837), 244–249 (2020)
Peng, X., et al.: Contour-enhanced CycleGAN framework for style transfer from scenery photos to Chinese landscape paintings. Neural Comput. Appl. 34(20), 18075–18096 (2022)
Santos, G.J.E., Rivera, M., Eiswirth, M., Parmananda, P.: Effects of noise near a homoclinic bifurcation in an electrochemical system. Phys. Rev. E 70(2), 021103 (2004)
Sobolev, I., Babichenko, S.: Application of the wavelet transform for feature extraction in the analysis of hyperspectral laser-induced fluorescence data. Int. J. Remote Sens. 34(20), 7218–7235 (2013)
Tian, Z., et al.: Rapid water quality assessment by micro laser–induced fluorescence spectrometer. In: Advanced Solid State Lasers. Optica Publishing Group (2019). Paper JTh3A.46
Turner, J.T., Floyd, M.W., Gupta, K., Oates, T.: NOD-CC: a hybrid CBR-CNN architecture for novel object discovery. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 373–387. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29249-2_25
Turner, J.T., Floyd, M.W., Gupta, K.M., Aha, D.W.: Novel object discovery using case-based reasoning and convolutional neural networks. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 399–414. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_27
Wang, H., Zhao, Z., Wang, Z., Xu, G., Wang, L.: Independent component analysis-based baseline drift interference suppression of portable spectrometer for optical electronic nose of internet of things. IEEE Trans. Industr. Inf. 16(4), 2698–2706 (2019)
Yang, Z., Albrow-Owen, T., Cai, W., Hasan, T.: Miniaturization of optical spectrometers. Science 371(6528), eabe0722 (2021)
Zacharioudaki, D.E., Fitilis, I., Kotti, M.: Review of fluorescence spectroscopy in environmental quality applications. Molecules 27(15), 4801 (2022)
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)
Acknowledgements
This document is the results of the research project funded by the Science Foundation Ireland (SFI) [SFI/12/RC/2289_P2]; Beijing Dublin International College Fund; China scholarship council Grant 202106120101.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Du, H. et al. (2024). Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1969. Springer, Singapore. https://doi.org/10.1007/978-981-99-8184-7_10
Download citation
DOI: https://doi.org/10.1007/978-981-99-8184-7_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8183-0
Online ISBN: 978-981-99-8184-7
eBook Packages: Computer ScienceComputer Science (R0)