As a music-theory-unsophisticate, I foresee a lot of potential for deep learning in creating musical arrangements for existing compositions using all kinds of a

As a music-theory-unsophisticate, I foresee a lot of potential for deep learning in creating musical arrangements for existing compositions using all kinds of available instruments. How might a Beethoven symphony be arranged for a typical 4 part rock band? What if you added a piano? What other instruments might add further value, esp. toward certain ends, like when learning to play an instrument? Could a deep net learn to accompany adaptively, as only a capable human can today? Could it offer instructional suggestions for improvement, especially subtleties in timing or dynamics? I also see value in transferring musical style for a piece from one genre to another (let's say, from jazz to rock & roll). Or from one set of instruments (or voices) to others. If done well, that could provide a lot of potentially entertaining interpretations or mashups, or at worst some novel medleys. Sinatra raps. Nat King Cole does bluegrass. Because examples of uses like these aren't likely to be plentiful, I suspect these won't be so much supervised learning tasks as reinforcement learning (RL) tasks. The trick then will be to somehow construct useful (and insightful) reward functions for each new use -- basically aesthetic meta-learning for RL. If RL reward functions can learn to write winning advertising jingles, it's hard to imagine what creative doors they won't open, eventually.