[PD] Waveform Analysis?

[Hans-Christoph Steiner]

There has been a number of things done with Markov models, but from my 
experience, the music generated with them always end up sounding like 
strange synthetic imitations.  A lot of algorithmic composition is 
based on Markov models.  Music made with Markov models seems much more 
interesting when Markov models were used as the medium of expression 
rather than imitation.

musicbrainz might be interesting to you.  Its basically a method of 
fingerprinting audio so that it can be identified even in different 
bitrates, recordings, etc.  http://www.musicbrainz.org/MM/  There was 
some other similar software too, but I can't remember the name now.

If you want to build your own system, you can start with [bonk~] and 
[fiddle~] for percussive and pitched note detection.

.hc


On Thursday, Apr 15, 2004, at 09:13 America/New_York, Ian 
Smith-Heisters wrote:

>> I wonder how much Markov-chain modeling would be appropriate for this.
>> I
>> mean, if music is a language, then why not analyse it using Natural
>> Language Processing (NLP) techniques like Markov's... :-)
>
> I'm not familiar with Markov-chain modeling, but it might be an 
> interesting endeavor to try to describe genres
> of music using first order logic ;). I suppose any of these methods 
> could work, it's just a matter of which is
> easiest, and describing musical genres using logical assertions sounds 
> even more difficult than trying to do it
> in natural language, which has already proven almost impossible, esp. 
> with 20th century music.
>
> A lot of people say that music is a mathemetical language so a more 
> mathematical approach may tend to be more
> efficient. Then you can leave the details up to the algorithm, once 
> you've given it the tools to analyze the
> music and a thousand pre-defined songs as guidelines. But I guess 
> that's exaclty what looking at it as natural
> language, and analyzing it with Markov chain modeling would be. Once 
> you've defined your language of notes,
> measures, tempos etc (a much smaller vocabulary than traditional 
> natural language) you could let the modeling
> search go at it. Or something. I only have a cursory understanding of 
> AI, and all my practical knowledge is
> limited to search techniques and propositional logic. I'm just musing 
> at this point. Now, we just need to do
> this all on the fly with my new extern [genrefi~] :) It takes a signal 
> input and outputs the song's genre and
> aesthetic quality on a scale of 1-10. You can watch your quality 
> rating go up and down as the song progresses.
> I'll never write a bad song again.
>
> Actually, when I have some time I was thinking of sitting down and 
> writing an algorithm that chops up sound
> files and reasembles them. It would base its 'choices' on some desired 
> attributes (bpm, climactic placement,
> choppiness/smoothness (1-10) etc.) and on some vague definitions of 
> what is 'good' and 'bad' music. The idea is
> that I could feed in any sound file, eg. birds chirping, or some 
> abstract ambient improv and it would cut it in
> such a way that it becomes a structered, rhythmic song of some sort. 
> Really more of an excercise in genetic
> algorithms than anything practical.
>
> -i
>
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                                     http://at.or.at/hans/


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