Re:Generating classical music with LSTM neural networks
by mcleavey on Wednesday November 28, 2018 @02:46PM
(#18554963)
That's so interesting - you're totally right that setting constraints often leads to really creative ideas. It reminds me of the "crab canons" by Mozart and Bach: https://en.wikipedia.org/wiki/Crab_canon .
I also think there's room for other creative encodings for music - possibly expanding these notewise/chordwise ideas, or possibly going in a totally new direction. It's fascinating to me how much the generations are affected by the encoding.
Re:Generating classical music with LSTM neural networks
by evrydayhustling on Wednesday November 28, 2018 @03:45PM
(#18555500)
Another fun direction is to generalize the kinds of constraints we put on our own instruments! I had a chance to play with that in a graduate class by implementing an API for midi generation where you set chord fingerings and strum patterns independently for a guitar of [N] strings.
Of course, I had to "play" the guitar myself by writing song sequences in those terms... it would be terrific to see what an AI could do with a notation scheme representing, say, a 20 string guitar or a 30 foot long flute.
Re:Generating classical music with LSTM neural networks
by williamdclt on Thursday November 29, 2018 @06:33AM
(#18559482)
Baudelaire said something like that (about the sonnet): "Parce que la forme est contraignante, l'idée jaillit plus intense" (poor translation: "because the form is constraining, the idea comes out more intense")
Re:Generating classical music with LSTM neural networks
by nixpulvis on Wednesday November 28, 2018 @07:34PM
(#18557107)
I forget where I read it, but that's the design behind the OP-1 by teenage engineering. https://www.teenageengineering.com/products/op-1
Re:Generating classical music with LSTM neural networks
by emptybits on Wednesday November 28, 2018 @03:56PM
(#18555604)
I reckon Igor Stravinsky would agree:
"The more constraints one imposes, the more one frees one's self. And the arbitrariness of the constraint serves only to obtain precision of execution."
Ages ago I was a sequencer geek (Impulse Tracker!) while also noodling around with guitar, and I noticed something strange: I made music I liked a lot more when I composed on guitar and transposed onto the sequencer afterwards. After a lot of experimentation, I realized that the constraints on what my hands could do on guitar were (of course) having a huge impact on what I tried to do when composing -- and struggling with the constraint was helping me make music I liked more.
I like a vision for practical machine learning where we spend less time on plumbing and more time thinking about the kinds of constraints (e.g. through input encoding) that enable "creativity" on the part of the machine.