How to calibrate a signal with respect to frequency I have recordings from accelerometers (non-flat response with respect to frequency) and I'd like to convert my voltage time series into acceleration values.  Is there a standard method for doing this?

My proposal to achieve this is:


Break the signal into equally spaced segments overlapping with 50%.
Apply a fft (with appropriate window) to each segment and apply the calibration values to each frequency.
Convert the segment back into a time series by applying an ifft and reversing the window transform.  We not have calibrated segments of the time series, although they are likely disjoint between window segments.
Apply a triangle window to all overlapping segments and add them into a single time series.  The triangle window should result in smooth transitions between window segments, resulting in a continuous, calibrated time series.


Are there any problems with this approach?  The window choice is dependent on use case.  Are there other approaches to achieve the same goal?
Potentially a lot of problems.

You really want to apply a filter to your data that the calibration curve represents, which seems to be your conceptual approach, only frequency domain filtering doesn't involve overlapping and windows, which one does in spectral estimation.

The hard part is to convert your calibration curve into a filter.