(Michael Chinen)

Just in time for the 2008. We’ve been working more diligently than an imaginary development log reader might think. And not just on such great koans as “what thought does an imaginary development blog reader upon reading an imaginary blog with an imaginary subject,” but also on our genetic algorithm. We’ve fixed a lot of bugs in the GUI, but besides that, completely changed the spectral estimate function by doing both real hard empirical tests to create an integral that was both simple enough to run in good time and mapped well enough on to the actual synthesized spectrum it makes.
The other real big change was the implementation of a cache. It is a two dimensional backwards and forwards extra-fancy grade cache that uses extra-fancy hit ratio tracking. The basic idea is that our variable length chromosomes grow bigger and bigger, and so that a single mutation is likely only to change a small part of the sound, in either frequency space, or time space. So what we do is cache every frame’s estimate, and use it if the interpolated parameters match. BUT WAIT, THERE’S MORE! The spectral estimate for a given frame is the sum of the integrals for each noise band. So if a noiseband in the middle of the thing changes, we can’t use the cache’d frame. So what we do is cache the say, 20%,40%,60%, and 80% noiseband marks, and do the same for reverse. So if the 55%th noiseband changes, we go back and add the forward 40% cache and backwards 40%(which is really 60-100%) and then from there manually caculate the integral for the the 40-60% mark and add those. In otherwords, this doesn’t make any sense unless you, fortunately like we did, upgraded our imaginary blog readers to the telepathic-understanding-cache-imaginary-dev-log-readers type.

Also if you’ve been reading the forums, biological evolution seems like it is optimal at an average of 1 mutation per chromosome. It turns out that works well for genetic algorithms too, so we’ve implemented that.

We still have a ways to go, but we can do noisy sounds, such as wind now. Female speech is a long way yet to go, mostly because we need more dynamic window functions (hint hint).

Wind experiment directory of wav synthesis and original for wind/screen source.

Female Speech experiment Bad, but understandable female speech experiment. original and subsequent generations.

Today would have not lived up to its reputation as a friday the thirteenth had we not just finished a new method of synthesis for our engine: sinusoidal based noisebands. And I’m talking full bandwidth noise bands that are not weak wussy filter generated noise bands with their unpredictable phase and amplitudes. I’m talking about grown men monotonic in phase sinusoids that have advantage of being obedient when you want them to be. Last week we did four hours of googling and found no obvious similar techniques, so this small synthesis component may actually be worthy of publishing. However, for your grain of salt, three and four fifths an hour of that time was spent self googling. Have a listen for your self to decide how sweet this is:

Noise Band examples

Sinusoids that move in amplitude and frequency randomly generated envelopes, with a bandwidth envelope that goes from fully open to fully closed, gradually opening up over one minute

80hz Noise Band

400hz Noise Band

1000hz Noise Band

10000hz Noise Band

There’s some sweet new open source projects on sourceforge, some of which we might have been partly responsible for. The benefits are of course, CVS and public incentive.

check it.

also, here’s some
older installation music that never got posted.

It has been a few months since our last post. We’ve been showing off music at places. Sometimes it was relevant to this research, like the new piece that uses the GA output to make all its noise. In all, we went to three cities in Japan and played music.

However, we are a little happy to get back to research. Lately in our heads we have been thinking of using some kind of subtractive model to make the fitness function really speedy. This still relies on using the FFT. Some of us are thinking to look more into mp3 and ogg encoding to figure out how they represent their spectral data. The sinusoids are getting a little harsh, and if you’ve heard that piece, you’ll know what we’re talking about. Anyway, hang in there, we’ll post more later.

In the middle of august we presented our current state of research to a accoustic research society. But as a reader it is not clear what the current state of our research is and apologize about the sentance order used here. Since the last update, a lot has been accomplished – we can now resynthesize certain sounds like cello within a few decibels of spectral distortion. However, we have become to think this sinusoidal based model kind of rather sounds like shit, and are looking for other ways to produce sound. However the results of the current model are below for your listening pleasure.

Last week we demonstrated our genetic algorithm sounds in a real time interactive system that uses four light sensors. We recorded lots of insects in shikoku, analyzed just two of them with our genetic algortihm, and used the output sound to do a 4 channel sound space. It was done in a week using supercollider, so the results are so so. Here is a picture of interested looking and uninterested-looking Japanese playing with it. Listen to the sound below.

The New Deal

Everything has changed. I’m panicking. The structure of the chromosome is being ripped apart to make way for new interpolated sound generators that guarantee some smoothness. Listen:

We had to present some kind of demo with our incomplete software for an open campus event to attract high school students. So this is what we spat at them besides our horrible Japanese.

Much more work has been done to the algorithm, but it is mostly bug fixing. The Fitness Functions have been implemented, using FFTs and RMS values. A bad gaussian noise unit gennerator has been implemented, yielding bad, but existing results. The good thing is that now we have enough to start fitting sounds to other sounds. The bad thing is that the algorithm needs to be fixed up to be able to process longer sounds (See the example below). Some work has already been done on this, making all audio buffers used by the GA requiring the use of a custom memory manager, which shares and recyles memory efficiently, but more is needed.