  In a normal indoor environment, Raman spectrum encounters noise often conceal
spectrum peak, leading to difficulty in spectrum interpretation. This paper
proposes deep learning (DL) based noise reduction technique for Raman
spectroscopy. The proposed DL network is developed with several training and
test sets of noisy Raman spectrum. The proposed technique is applied to denoise
and compare the performance with different wavelet noise reduction methods.
Output signal-to-noise ratio (SNR), root-mean-square error (RMSE) and mean
absolute percentage error (MAPE) are the performance evaluation index. It is
shown that output SNR of the proposed noise reduction technology is 10.24 dB
greater than that of the wavelet noise reduction method while the RMSE and the
MAPE are 292.63 and 10.09, which are much better than the proposed technique.
