On Saturday 17 April 2004 03:22, Ian Smith-Heisters wrote:
just like Markov Chain modeling does, the difference in the two filesizes is directly porportional to the amount of difference in the two songs. The advantage (I guess) is that there is much less specialization required in writing the analysis functions. In twenty words or less.
I read about something like that, but they were analysing mididatafiles compressed with zip. So there was allready a high level of information there. The pitches and all. I wonder if it works for a compressed WAV file.
we've gotten off topic. On my part this digression sprouted from wondering what to do with all the waveform analysis data once you have it.
That is the big question of course. Raw analysis data is too much anyway, you need to reduce it somehow and then what....?
You could use it generate a midi-like stream of notes and compare that to something. And then use the amount of similarity to do something else. Or you could try to generate sound that is as different as possible.
What those somethings are is were the creativity of the composer comes in. Because IMHO programming a patch that makes music using this, is composing as much as putting notes on score paper or sounds on CD.
gml@xs4all.nl:
Besides the analytical tools with orthonormal function-spaces (fourier etc.) I am also looking into classification based on the geometric/topological properties of the wave. Things like the fractal dimension of the waveform over either a short window, like fft's, or on longer segments. They use this kind of stuff to analyse heart-rythms f.i.
I would be interested in any links you might have, as I'm unfamiliar with this kind of analysis.
I looked into this before Christmas and then was to busy to follow up on it. It hasn't been used in musical analysis that I know of. But if you google for "fractal dimension" and "periodic signal" you get a lof links. I am still not sure if it _is_ useful at all, but I got some pretty strange results that I liked and I will continue trying this out.
Gerard