So is there an analagous process that would apply to audio I wonder?
This is actually training a neural network on the Markov model, so it's very similar to core ideas behind the OP's paper. The core idea is to model the probability of a bit of sound by breaking it into the last note and everything that comes before the last note ("P(audio)=P(audio∣note)P(note)"). If you sample a bunch of audio and factor it that way for any given point in time, and accumulate that data somewhere, you can then sample the accumulated data randomly to generate new music.
There are other audio NN synthesis methods as well, pretty sure I've even seen one posted to ShowHN before.
For example an approach similar to https://gauthamzz.github.io/2017/09/23/AudioStyleTransfer/ but then using the methods described in the PixelNN paper.
It more or less attempts to be what you describe. Not very polished yet, but I had some basic success in modeling the parameter space of a synth, and adding new latent spaces with regularization.
(Eg first result: http://sunbeam.ece.wisc.edu/csaudio/)