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Yes, you can apply deep learning to peak detection. A 1D CNN would be appropriate for this task. Here is an example for such application: Risum, Anne Bech, and Rasmus Bro. "Using deep learning to evaluate peaks in chromatographic data." Talanta 204 (2019): 255-260. You would need to have annotated data. If you decide to stick with the classical ...


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I think the most straightforward basic approach to detecting noise/variance levels is: highpass -> rectify (or square) -> lowpass There are many types of 'highpass' or 'lowpass' like operations/filters that you can choose from and tune based on your application. Below I use diff (e.g. derivative) effectively as a highpass filter, and a couple rolling ...


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