GAN-based simulation Raman spectrum data generation method

YoungJae Son; Tiejun Chen; Sung-June Baek

doi:10.1117/12.3050680

27 September 2024 GAN-based simulation Raman spectrum data generation method

YoungJae Son, Tiejun Chen, Sung-June Baek

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 1328107 (2024) https://doi.org/10.1117/12.3050680
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

In this paper, we propose a data generation method for background noise processing of spectral signals. Spectral signal data cannot be easily collected, so various data generation methods are being researched. The previous method implemented peak and background noise using kernel functions and polynomials. This method is similar to real spectrum data, but peaks are modeled globally, making peak identification difficult. Also, the polynomial method has limitations in implementing the complex background noise of the real spectrum. In this study, we focus on generating data using Generative Adversarial Network (GAN). GAN is a popular deep learning generation model using a generator and discriminator. In this study, data is generated using chemical Raman spectral library data. Afterwards, the designed background noise is added and trained on the baseline correction model using deep learning. Afterwards, it was applied to raw spectrum data and confirmed that it can be effectively applied to raw spectrum as well.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

YoungJae Son, Tiejun Chen, and Sung-June Baek "GAN-based simulation Raman spectrum data generation method", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 1328107 (27 September 2024); https://doi.org/10.1117/12.3050680

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KEYWORDS

Background noise

Gallium nitride

Data modeling

Deep learning

Raman spectroscopy

Education and training

Signal processing

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