Peak Detection and Baseline Correction Using a Convolutional Neural Network

Abstract

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.