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by dwringer
1345 days ago
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I'm not familiar enough with existing implementations of such systems to dispute it, but there's no fundamental reason algorithmic composition systems could not include modulation parameters of all kinds (pitch/breath/effects/synthesizer controls/etc) in their output. I am envisioning a DAW set up with several VST's and samplers with routing and effects in place, then using some combination of genetic algorithms and other methods to "tweak the knobs" in the search for something pleasing. The search space is absolutely enormous, though, so I don't dispute that it's very difficult, but I wouldn't go so far as to say that it can't be done. In such a space there are "no wrong answers" so to speak. I have a python script which creates randomized sequences of notes/rhythm and gives each one a different combination of LP/HP filters and random envelopes - it's not music but it takes on a much less mechanical quality by emulating different attacks and timbres over time, even though it's completely random. I would go so far as to say I'd be genuinely surprised if algorithmic composition and production hasn't been used to some extent significantly greater than "basically a piano roll" in at least some of the past decade's top 40 music on the radio. |
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There is such a reason - lack of training data. Very few high quality detailed MIDI samples exist to train machine learning models like AudioLM.
For state of the art in MIDI generation, take a look at what https://aiva.ai/ produces (it's free for personal use). There you can compare raw MIDI output to an automatically generated mp3 output (using "VST's and samplers with routing and effects in place, then using some combination of genetic algorithms and other methods to "tweak the knobs" in the search for something pleasing.")
mp3 version will sound much better than raw MIDI, but (usually) significantly worse than music recorded in a studio and arranged/processed by a human.