> I think if you want it to be classical music then it has to obey the rules of counterpoint, and the pitches should wander and resolve according to those rules.

Uh, that's polyphonic music. There's plenty of classical music that isn't particularly polyphonic. Most of post-Baroque music is more homophonic: voices tend to move together in chords rather than independently. Counterpoint still appears but it's not the foundation.

counterpoint is also used for the individual voices in the chords. you don't just bang out one chord after another. that's why you switch between different inversions as the chords progress - to avoid parallel 5ths etc

also in this case he is not working with chords. they are just pitches moving.

Yes. Sorry, but as a music geek, this is actually pretty terrible - not even close to bad first year composition student pastiche, and a long way short of David Cope's EMI, which is probably the current state of the art.

I wish coders who are trying to do something in a creative domain would learn the basics and not just assume they can throw some simple algorithms at an artform and get anything close to an acceptable result.

No one is going to take a coder seriously if they can't code fizzbuzz.

Here's a thing to know: all the arts have their own equivalents. If you don't know what they are, learn them. Then maybe you can start thinking about non-toy algorithms and data structures that are going to impress an audience that cares more about quality of output than implementation details.

Most people who start working with domain-specific knowledge find it's much harder than they think.

This isn't interesting because it generates the best algorithmic music, it's interesting because he trained it on data, and didn't implicitly encode (very much) music theory into it. In other words, the fact that it sounds musical at all is due to the power of the neural network, and not to a carefully human-curated set of music theoretical constraints, onto which an RNG selects the remaining free variables. It's learning music from reading music and it's generating music based on a model constructed from the data itself.

But that's not actually true. It's a note sequence mash-up machine, not a music theory machine.

That's the point I'm making. You'd get similar-sounding results by taking semi-random snippets of the source data and splicing them together with a tiny bit of glue logic.

The NN is more or less doing that anyway, but by more roundabout means.

It's a long way from there to being able to say that it has a non-trivial model of classical theory.

The point of this type of modeling is to see how far a black box can get. I don't think anyone is claiming this LSTM is creating "state of the art" art.

Although I agree with you about the music part, truth is the post actually is about proving the oposite. Sure you have scales and counter point and harmony to care about, but given enough computational means, a machine will eventually figure them out from previous examples without knowing the theory.