Hi @jan,
Spectral flatness often correlates to how “noisy” a spectrum is–the flatter the spectrum the noisier the sound.
As for skewness and kurtosis, these get more abstract in terms of how they might be perceived and “resynthesized”. Also, they might start to compete with other parameters that you’re using to drive filters or whatever (as suggested by @jamesbradbury) such as the spectral centroid and spread, leaving strange results. Maybe just choosing a few of the analysis parameters to drive a filter and/or some other analyses to drive some other synth params will be more straight forward.
Another thought is you could use these analyses as the input to a FluidMLPRegressor and train the neural network to predict synthesis parameters for whatever synthesis algorithm you want to use. In order to do the training you would need to pair a bunch of synthesis parameters (probably ones that are chosen by and important to you) with the SpectralShape analysis that those synthesis parameters produce. Then train the neural network using the analyses as the input and the params as the output.
Example file attached. This one uses MFCCs instead, but I’d be curious to see how it works with SpectralShape! It might be better! Also this is using just FM Synthesis, so putting a more interesting synth algorithm in there would be cool as well!
Let me know if you give this a shot, I’d be happy to help!