scholarly journals Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4623
Author(s):  
Sinead Barton ◽  
Salaheddin Alakkari ◽  
Kevin O’Dwyer ◽  
Tomas Ward ◽  
Bryan Hennelly

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

2002 ◽  
Vol 13 (01) ◽  
pp. 038-049 ◽  
Author(s):  
Gabrielle H. Saunders ◽  
Kathleen M. Cienkowski

Measurement of hearing aid outcome is particularly difficult because there are numerous dimensions to consider (e.g., performance, satisfaction, benefit). Often there are discrepancies between scores in these dimensions. It is difficult to reconcile these discrepancies because the materials and formats used to measure each dimension are so very different. We report data obtained with an outcome measure that examines both objective and subjective dimensions with the same test format and materials and gives results in the same unit of measurement (signal-to-noise ratio). Two variables are measured: a “performance” speech reception threshold and a “perceptual” speech reception threshold. The signal-to-noise ratio difference between these is computed to determine the perceptual-performance discrepancy (PPDIS). The results showed that, on average, 48 percent of the variance in subjective ratings of a hearing aid could be explained by a combination of the performance speech reception threshold and the PPDIS. These findings suggest that the measure is potentially a valuable clinical tool.


2014 ◽  
Vol 22 (10) ◽  
pp. 12102 ◽  
Author(s):  
Shuo Chen ◽  
Xiaoqian Lin ◽  
Clement Yuen ◽  
Saraswathi Padmanabhan ◽  
Roger W. Beuerman ◽  
...  

2014 ◽  
Vol 10 (S306) ◽  
pp. 292-294
Author(s):  
Haijun Tian ◽  
Yang Xu ◽  
Yang Tu ◽  
Yanxia Zhang ◽  
Yongheng Zhao ◽  
...  

AbstractWe propose and preliminarily implement a data-mining based platform to assist experts to inspect the increasing amount of spectra with low signal to noise ratio (SNR) generated by large sky surveys. The platform includes three layers: data-mining layer, data-node layer and expert layer. It is similar to the GalaxyZoo project and it is VO-compatible. The preliminary experiment suggests that this platform can play an effective role in managing the spectra and assisting the experts to inspect a large number of spectra with low SNR.


Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 841 ◽  
Author(s):  
Di He ◽  
Xin Chen ◽  
Ling Pei ◽  
Lingge Jiang ◽  
Wenxian Yu

Noise uncertainty and signal-to-noise ratio (SNR) wall are two very serious problems in spectrum sensing of cognitive radio (CR) networks, which restrict the applications of some conventional spectrum sensing methods especially under low SNR circumstances. In this study, an optimal dynamic stochastic resonance (SR) processing method is introduced to improve the SNR of the receiving signal under certain conditions. By using the proposed method, the SNR wall can be enhanced and the sampling complexity can be reduced, accordingly the noise uncertainty of the received signal can also be decreased. Based on the well-studied overdamped bistable SR system, the theoretical analyses and the computer simulations verify the effectiveness of the proposed approach. It can extend the application scenes of the conventional energy detection especially under some serious wireless conditions especially low SNR circumstances such as deep wireless signal fading, signal shadowing and multipath fading.


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