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From the mathworks documentation of the function envelope(): The filter is created by windowing an ideal brick-wall filter with a Kaiser window of length fl and shape parameter $\beta = 8$. So without hacking the function you can't directly get the filter coefficients, but you can easily find them yourself by just doing what they do, i.e., windowing the ...

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Non-linearity and non-stationarity are non-properties. Without more details, they do not say much about the methods that may perform well, and moreover the choice depends a lot on what you really do: analysis, feature extraction, enhancement, filtering, component separation, restoration? What follows are typical sets of tools you could use: Your moving-...

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Your code does what you ask it do and the result look fine to me. It would be helpful to state why you think this is wrong and what you did expect instead. Envelope detection using Hilbert Transform works well for narrow band signals, (Amplitude modulation for example), but it typically does not do well for broad band signals as you do have here. You ...

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Hm well, technically it is some kind of envelope: it oscillates between hilbert(x) and -hilbert(x). Your examples (dashed lines are $\pm$hilbert(x)): I'm assuming you're looking for something smoother. Matlab has a function called envelope where you have various ways of controlling how the envelope is extracted. Not sure if there is a Python equivalent. ...

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