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Comment by bane | Hacker News Reader

I agree, I kind of feel like there should be a couple different layers of generation.

A "planning" layer that lays out the song plan (ABACABBA, etc.)

A composing layer that fills in those sections. And maybe even generates some slight differences between the same-named sections for variety.

A performance layer that plays it back with a simulation of human performance metrics (slight jitter to note placement, emotive crescendos, suggestive variations in note-length, etc.).

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Maybe this kind of thing can also be learned by a secondary NN. It just needs to be trained with data collected over large scale sections of the example music.

But this NN doesn't solve the greatest problem in Classical music: that only 3% of people take the time to appreciate it.

Let's design a neural network to appreciate classical music, then spawn a few billion instances. Greatest problem: solved.

http://xkcd.com/1546/ comes to mind.

Why do you think that is the greatest problem? If a concern for audience numbers, what's the goal and why? In classical music, my experience is that there are many more highly accomplished practioners than can ever be supported by audience demand but that's only a problem if there's an argument for a set goal for audience figures. There are of course thousands of pursuits which demand time for their appreciation.

Greatly more than 3% of people appreciate classical music. I've been to countless sold out classical symphonies. It may not be their favorite genre, but people pay attention and enjoy it. It's only 3-7% of new music sales in stores, which is all that media wants you to think about.

For pop-rock and other styles of mainstream music there are massive libraries of chord progressions that analyse all the "hits" structure (or big picture). Those would be really helpful with the "planning" layer... I.e:

http://amitkohli.com/wp-content/uploads/2015/02/InteractiveC...

I bet you can get results much more accurate feeding all those progressions as one of the reference layers.

There is a series of works of Francoise Pachet at sony research labs in Paris and his collaborators that uses hmm with constraints to obtain something that preserves long-range structures. I don't have the links at the moment, but he got impressive results.

Will "NN Teacher" be a job title soon? In the present situation we have an NN who learned empirically and who needs a bit of structure: "Here are the sound plans you need to study, here are the melodies, here are the styles and trends".

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