Conference paper
Peak Detection and Baseline Correction Using a Convolutional Neural Network
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Drug Delivery and Sensing, Department of Health Technology, Technical University of Denmark3
Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Department of Health Technology, Technical University of Denmark4
Technical University of Denmark5
Peak detection and localization in a noisy signal with an unknown baseline is a fundamental task in signal processing applications such as spectroscopy. A current trend in signal processing is to reformulate traditional processing pipelines as (deep) neural networks that can be trained end-to-end. A trainable algorithm for baseline removal and peak localization can serve as an important module in such a processing pipeline.
In practical applications, one of the most successful approaches to joint baseline suppression and peak localization is based on the continuous wavelet transform: We re-formulate this as a convolutional neural network (CNN) followed by a non-linear readout layer. On a synthetic benchmark we demonstrate that with sufficient training data, the CNN approach consistently outperforms the optimized continuous wavelet method by means of adapting to the spectral peak shape, noise level, and characteristics of the baseline.
The CNN approach to peak localization shows great promise, as it can more efficiently leverage data to outperform the current state of the art, and can readily be extended and incorporated as a module in a larger neural network architecture.
Language: | English |
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Publisher: | IEEE |
Year: | 2019 |
Pages: | 2757-2761 |
Proceedings: | 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing |
ISBN: | 1479981311 , 147998132X , 147998132x , 9781479981311 and 9781479981328 |
ISSN: | 2379190x and 15206149 |
Types: | Conference paper |
DOI: | 10.1109/ICASSP.2019.8682311 |
ORCIDs: | Schmidt, Mikkel N. , Alstrom, Tommy S. and Larsen, Jan |
Benchmark testing CNN approach Continuous wavelet transforms Convolution Kernel Neural networks PSNR baseline correction baseline removal continuous wavelet transform convolutional neural nets convolutional neural network joint baseline suppression nonlinear readout layer optimized continuous wavelet method peak detection signal processing signal processing applications wavelet transforms