Noidypoos on 5/1/2008 at 12:31

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A project I developed last year was a piece of software in MAX/MSP that analysed the R/G/B/A matrices of an incoming video signal and mapped it to an additive synthesis engine which was driven by a "random" note generator that approximated human playing using second and third order Markov chains.

It was horribly, horribly complicated though

http://img140.imageshack.us/img140/8144/p02xu3.png

http://img171.imageshack.us/img171/1238/p05yu2.png

http://img411.imageshack.us/img411/7161/p08pn3.png

I hope those meaningless screenshots give you some inspiration. Taking the frame data from a camera and using it to control the timbre of a synthesised note is a pretty orthodox thing these days but I have several points to make:

1) ANYTHING can be used as source input. If you're working with a digital system, as long as you can convert the stimulus to a stream of numbers in some form, you can control it and use it to "power" your system. A friend of mine developed an amazing system that did edge detection on fucking CLOUDS and used the information to power an FM synthesiser.

2) Wolfram Tones is shit and there are far more powerful approaches to generatively sequenced peices. In fact I wrote something about the process I used ages ago (see (http://en.wikipedia.org/wiki/Markov_chain) here):

Markov chains are employed in algorithmic music composition, particularly in software programs such as CSound or Max. In a first-order chain, the states of the system become note or pitch values, and a probability vector for each note is constructed, completing a transition probability matrix. An algorithm is constructed to produce and output note values based on the transition matrix weightings, which could be MIDI note values, frequency (Hz), or any other desirable metric.

A second-order Markov chain can be introduced by considering the current state and also the previous state, as indicated in the second table. Higher, nth-order chains tend to "group" particular notes together, while 'breaking off' into other patterns and sequences occasionally. These higher-order chains tend to generate results with a sense of phrasal structure, rather than the 'aimless wandering' produced by a first-order system.

There are some very cool things you can do with Markov chains (see (http://www.eblong.com/zarf/markov/) here) and I urge you to investigate them.

3) If you're going to use frequency-domain analysis via a Fourier transform, investigate the discrete-time "versions" that are used in digital system. The "Fast fourier transform" is a particularly popular algorithm.

4) In general you have to be quite "strict" when dealing with generative systems in order to not let them spiral out of control. If you leave too many factors to chance you just end up with unlistenable bilge. What people are really interested in is having some impact on a sonic environment - "interfering" and changing the course of the rhythm, timbre and pitch in some way.

There are so many cool project ideas, you could go on forever. I'm currently devising a way to model the physics of extremely bouncy balls in a room, whereby each ball is assigned a note. Users can introduce as many balls as they like into the room, and each ball generates a note where the timbre is determined by velocity upon impact, e.t.c.

I don't know why I made this post I just did it for you frozenman because you're special

demagogue on 5/1/2008 at 21:37

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I studied a little about Markov chains in a totally different context (statistical language gen), but now that I think about it they'd also be great for music generation ... harder to normalize, maybe, but you can get much more complex and interesting patterns out of them.

Stitch on 6/1/2008 at 16:58

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Chrissake gentlemen it's really not that difficult to learn how to write a song.

Also: convert odor to notes and that burrito I ate last night could sing you a tune :(

Stitch on 10/1/2008 at 18:55

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It's a slippery slope Nicky :cool:

No, I was just kidding. This thread has a certain dark power over me despite the fact that I only sort of understand what the fuck.

TTK12G3 on 10/1/2008 at 19:28

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It's the silent sound of the world!