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.
Original language | English |
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Title of host publication | Proceedings of 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 |
Publisher | IEEE |
Publication date | 1 May 2019 |
Pages | 2757-2761 |
Article number | 8682311 |
ISBN (Electronic) | 9781479981311 |
DOIs | |
Publication status | Published - 1 May 2019 |
Event | 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton Conference Centre, Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 Conference number: 44 https://2019.ieeeicassp.org/ https://www.2019.ieeeicassp.org/2019.ieeeicassp.org/index.html |
Conference
Conference | 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing |
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Number | 44 |
Location | Brighton Conference Centre |
Country/Territory | United Kingdom |
City | Brighton |
Period | 12/05/2019 → 17/05/2019 |
Sponsor | IEEE |
Internet address |
Keywords
- Convolution Neural Network
- Peak detection
- Peak localization
- Spectroscopy
- Wavelet