[microsound] AI in microsound

[david golightly <davigoli@xxxxxxxxxxx>]

I'm starting to develop a set of AI algorithms to assist in live 
performance, which will (roughly speaking) analyze control messages that I 
send and, after a certain point, be able to generate their own control 
messages based on the patterns I used.  Eventually I'd like to design an 
algorithm to analyze a continuously changing audio texture on multiple 
levels and be able to reproduce similar, non-repeating textures.  I've 
started by giving my PD patch some various recordings of birdsong (of the 
eastern Wood Thrush) for analysis, hoping to tune it to be able to generate 
its own non-repeating woodthrush-like song.

For discussion:  What AI algorithms are the most useful in microsound 
performance - in practice, not only in theory?  How many of you 
microsounders are using AI algorithms, and what have your experiences been?  
Ever used neural networks/second- and third-order Markov chains/genetic 
algorithms/generative grammars/other?  On what time scale (piece-structure, 
moment, sound-event, or microsound generation)? There was an interesting 
interview with Brian Eno back in the 1980's (with The Wire mag, I believe) 
where he expressed a desire for a musical "black box" that could produce 
music in a given style, a Beethoven black box, a Beatles black box, an Eno 
black box...  Beyond whatever proprietary concerns are involved, is this 
even achievable?  There's been a lot of talk of letting your laptop play a 
wave file vs. performing the music yourself, but does anyone have any 
experience teaching your computer to make desicions for you?

Cf.:
David Cope, Experiments in Musical Intelligence
Eduardo Reck Miranda (though I find his experiments with cellular automata 
contrived and unconvincing)
among others

thanks,
david



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