Fux wouldn't apply here because the bass/chord accompaniment shapes are idiomatic, and Fuxian counterpoint wouldn't be.
In fact this kind of toy ML can never produce competent music. The best it can do is produce short workable snatches that sound like cut and paste snippets of the training source - before losing the plot in the next bar or two.
Training an RNN on a huge set of tunes and expecting it to produce examples of equivalent musicality is a fundamentally unworkable idea.
I'd suggest that anyone who doesn't see why this must be true doesn't understand ML well enough to know when it can and can't be used effectively.
It's worth asking in what other domains are trivial RNNs being misapplied to produce trivially poor models.
It's one thing to make bad music. It's another to - say - run a trading strategy, or make marketing decisions based on oversimplified ML models that produce misleading results because they're not sophisticated enough to recognise all the critical structures in the data set.
In fact this kind of toy ML can never produce competent music. The best it can do is produce short workable snatches that sound like cut and paste snippets of the training source - before losing the plot in the next bar or two.
Training an RNN on a huge set of tunes and expecting it to produce examples of equivalent musicality is a fundamentally unworkable idea.
I'd suggest that anyone who doesn't see why this must be true doesn't understand ML well enough to know when it can and can't be used effectively.
It's worth asking in what other domains are trivial RNNs being misapplied to produce trivially poor models.
It's one thing to make bad music. It's another to - say - run a trading strategy, or make marketing decisions based on oversimplified ML models that produce misleading results because they're not sophisticated enough to recognise all the critical structures in the data set.