ADDENDUM, because I just realized I forgot a bunch of things I meant to put into this post

This is still a simplified model. It does not take into the Excitation plus Resonance model, the Spectral Voice Model. It uses a linear transform and not generated trajectories.

One thing I was thinking about was the part in WBVPM section where they said that one of the disadvantages of WBVPM was not being able to separate harmonic and non-harmonic. I also read that the noise is embedded as fluctuations in the spectrum of each voice pulse and over time, which is what I had presumed because the information has to go somewhere.

I was thinking, what if you took each harmonic as the values and the pulse onsets times as the positions in a spline. Then interpolated at regular intervals. Then applied the fourier transform. Then separate the highest frequencies and the others. Take the others and apply the inverse Fourier transform, and then rebuild a spline from this and interpolate the values back at the onsets. I wonder if this would work.

There would be loss though because of the resampling steps. This could decreased by taking more samples. You could also apply a correction by sampling and sampling it back to calculate the resampling loss itself without the removal of the high frequency modulations, and then add this difference back to the main pulse information after the separation.