Moved

For those still interested, the project development blog has been relocated to

www.anewmusic.com

airframe - Lindenmayer Upgrade

I've been working on airframe lately in hopes of getting the structure up to par with the other modules (progression module is my next target). I've come to the conclusion that generative modules need to be a lot smarter than they are right now, because they shouldn't rely too heavily on the structure module to provide "solid" structure data. Structures are really not very complex. To be perfectly honest, the best option would probably be a very simple KBS with a pre-programmed list of structure forms (ABABCB, etc). Until then, I'm overshooting the complexity. So the generative modules will be responsible for keeping the piece coherent and figuring out what to do if the structure modules sends overly-complicated (or overly-simplified) instructions.

The core system of airframe has been decided upon - at least for now. I'm proud to introduce it as the first mGen plugin to use a Lindenmayer system, also known as an L-system, which is basically a simple grammar system at first glance but has roots in fractals. I think the L-system will provide a refreshing dose of simplicity and organized complexity. I really won't know what to expect until I hear it, and if it works, it'll be far too good to be true given how easy an L-system is to implement.

Of course I plan on layering other processes over the L-system engine to refine the structure output (maybe an ear module?), so airframe will technically still be a hybrid plugin.

EvoSpeak is also still progressing.

EvoSpeak - Optimization

Yes, I'm STILL working on getting the analysis part of EvoSpeak working. I now have the structure of the species' brains figured out and I've optimized the analysis engine a LOT, thanks to the new storage method of the analysis filters. So things are looking pretty good and soon enough I should be working within the interface of EvoSpeak instead of grinding around in the code.

I'll admit, progress on mGen is coming slowly. It still looks like I'm going to make the code deadline that I set for Wednesday (17,000 lines), which is encouraging. How is mGen shaping up in the big picture/long run? That's what I'm more worried about. I'm going to have to step back and take a serious look at what's going on after I hit this deadline. I don't even really have anything for Mr. Taranto to help with yet, even though our meeting is in under two weeks. It's time to step up to the plate.

EvoSpeak - Analysis

Work is starting to get pretty messy with EvoSpeak. I'm trying to design a very generalized analysis framework to allow easy analysis of just about any relationship. Doing so is not at all easy. I'm trying to set up a "perception" matrix that simulates the state of any given sample at any given point in time. The idea is that an analysis function can built a filter matrix and then call the perception function, which will then compare the filter matrix to the perception matrix and gather some statistics.

The first analysis I'm designing is, of course, a zeroeth-order Markov. Fancy language aside, what it boils down to is this: did the species use a certain word (melodic or rhythmic) in a certain sample? So a zeroeth-order Markov simply deals with the innate "quality" of certain words over other words, not taking into account ANY contextual variables.

Problems are arising with this general framework. It's very difficult to obtain certain state values to populate the matrix because of the grammar engine design. The melodic and rhythmic data streams are asynchronous, so melodic events don't necessarily line up with rhythmic events, which makes finding synchronous data (like perception data) very difficulty. Apparently I've messed up in trying to separate the streams because some of the preliminary statistics are doing some strange things.

On top of all that, the analysis is a LOT slower than I thought it would be, even after serious reconstruction and optimization. I knew that it would take a lot of loops and recursions to do the analysis...but I thought the computer would just chew through them. Already a simple zeroeth-order Markov analysis on the melody alone costs about 2.6 seconds. Using that number and extrapolating, a second-order Markov analysis would take a whopping sixteen minutes, which is simply unacceptable. And that's only to level-up once. I'm definitely going to have to figure something out there.

While I'm running into some obstacles, EvoSpeak is still advancing steadily and I'm confident that the analysis functions will soon be fully-functional.

EvoSpeak - Experience & Dörf

I finished a lot of EvoSpeak coding tonight. The training process is mostly coded. The species will spit out samples, the user listens and grades the performance, and then the results are submitted and stored to the species' memory. It's also possible to create new species now from within the configuration utility.

I created my first creature, Dörf, today. He speaks the default language. Why the name? I'm not sure, I just liked it. I've trained him 24 times so far, so he has 120 xp. He's actually ready to level up to level 2, since it only requires 100 xp to do so. Well, I guess as soon as I finish making the leveling-up algorithm, (which is the real meat of this whole process since it provides the "brains" for each species) he'll be good to go.

I look forward to working with Dörf; I hope he's a memorable first EvoSpeak species.

EvoSpeak - Getting Closer

I finished the preview builder and now have a working random pattern generator and previewer for EvoSpeak. I still can't submit ratings so species don't gain experience yet, but the hardest work is done...until it comes time to build the "leveling" mechanism (i.e. the Markov analysis tool).

And the results of the initial grammar runs? Good! Overall, I am very satisfied with what I'm hearing. Based off of the twenty-or-so previews that I've listened to so far, the engine is much more interesting than GrammGen. It sounds a lot better.

The thing I really like, however, is that switching languages dramatically changes the previews. Of course the same was true for GrammGen, but I never built a second language for GrammGen because of the relative difficulty of editing the languages. In EvoSpeak there's a built-in language editor. It's as easy as slapping in some pipe-delimited numbers for rhythm and melody and listening to the results.

It took me thirty seconds to build a language that could be used for repetitive arps in the background. So I think I've found my solution for arpeggiation! The simple the language, the more likely it is to repeat words - which is exactly what you want in a background pattern. After listening to some previews of the new language, I'm certain that this will be a very promising and flexible system.

So far EvoSpeak is going very well! The real question, however, has yet to be answered: will the "experience" and analysis system actually allow EvoSpeak to improve the quality of its output? The answer would seem to be a very obvious yes if I do everything right. But at the same time, it's hard to believe that listening to samples and pressing buttons can train a program to make better music. But who knows, I guess I'll just have to find out.

PS - It's worth noting, in case I was never clear about this, that EvoSpeak is NOT a grammatical subdivision engine like GGrewve, rather, it's a grammatical chain engine like GrammGen. Chains are simpler and easier to work with but subdivision is more powerful. And yes, I coined both of those terms, which is why you won't find information on them anywhere else :)

EvoSpeak - Progress and Ideas

I'm still working on EvoSpeak, getting the engine all set up. I finished the random generating algorithms that will provide training material from which EvoSpeak will "learn." They also define the basis of the new grammar system, whose syntax is simpler even than that of GrammGen, but whose power is much greater.

Next I need to create the functions that will analyze the training material to figure out what attributes they have in terms of melody and rhythm. All of this analysis data will be stored in a training file that will also indicate how well the user likes the material. After a certain number of training pieces have been graded by the user, EvoSpeak will dig up all the analysis data and perform an extensive statistical analysis on it to try find correlations and develop a "brain," so to speak, that will allow the program to function as an output device.

I'm still trying to figure out exactly what variables/attributes should be part of the "brain." This has always been my problem with statistical models; I've never known exactly what variables to draw statistics from. Now I've got to tackle the issue. I'll start simple - state variables (such as what beat the rhythmic or melodic object falls on) and first-order memory variables (what the last rhythmic or melodic object was) should work fine for the first version.

I plan to have EvoSpeak set up in an intuitive "leveling" kind of way that reflects a simple game. Before EvoSpeak will work, the user must first create a new "creature" that speaks a certain "language." At first the creature will have no idea how to speak the language; like a child, the creature must be shown how to use words to make sentences. The user "trains" the creature by listening to samples and rating them on a scale of 1 (strong dislike) to 5 (strong like). The creature gains XP (experience points) when the user listens to samples and submits ratings. When the creature has enough XP, it can "level up." During the leveling-up process (unbeknownst to the user), the creature actually goes back and analyzes all of the samples and ratings and essentially "learns" from the previous batch of material. The leveling system is good because it will ensure that correlations are relatively strong before they will be used to generate (i.e. the creature won't work without the user having trained it to a certain level).

At higher levels, creatures may learn the ability to analyze deeper variables other than states and first-order memories. Perhaps the creature gains more memory with each level (this is equivalent to increasing the order of the Markov chain analysis). Or perhaps the creature starts analyzing surface contours (3-variable functions) instead of 2-dimensional dependencies.

These are pretty abstract and crazy ideas, but I think they make sense, and I think they will provide a refreshing and intuitive break from the usual grind of KBSs. I'm interested to start training my first creature! And if the leveling system actually makes the music sound better (as intended)...well...I think I could spend all day leveling my creatures (is this starting to sound like Pokemon? That's neither my intent nor my inspiration).