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arXiv Machine Learning By Jihwan Kim, Jaeseong Heo, Yujin Kim, Sang Bin Yoon, Chanjoong Kim, Kwang-Im Oh, Hyon Bin Na, Kyungwon Kwak, Myeongkee Park, Joonyoung F. Joung

Wavenumber-Aware Deep Learning for Robust Analysis of Raman Spectra

Read the original on arXiv Machine Learning →

The paper introduces a wavenumber-aware deep learning approach that represents each Raman spectral point as an intensity–wavenumber pair and uses Transformer positional encodings derived from the wavenumber. This method remains robust to noise, sparse sampling, and partial spectral coverage across various neural network architectures, outperforming index-based and learned encodings when only part of the spectrum is observed. The approach was validated on Raman spectra from three bacterial species, their synthetic mixtures, and a public 30-isolate bacterial benchmark, demonstrating consistent performance on unseen sampling grids.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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