Grammar: Success?

Well, after coming back from a long break, I have some good progress to report.

I tried implementing a grammatical system for algorithmic composition over the break and had a good deal of success in my endeavors. Although I created only a rudimentary composition language and a very basic phrase generator, the results seem more natural and interesting than any other method explored thus far, which means progress!

After a great deal of thinking on the subject, I've decided that a grammatical system might be the key I've been looking for to a successful path to my goals. The trick is that I can use a grammatical system as the underlying paradigm for other methods. In other words, I could have an evolutionary model that uses an underlying higher-level grammar to generate phrases. In theory, this idea is really no different than using an evolutionary model to generate an equally-abstract number that corresponds to a certain pitch (a MIDI note event). I could do the same thing with Markov chains and state-transition matrices.

There are a lot of places to go with grammatical algorithmic composition. I have a feeling I'm just scraping the surface of something big. Let's hope I'm not let down.

New Resource Links

IBM: Music Programming and Algorithmic Composition
Developer space for a wide variety of algorithmic composition resources, tools, etc. Very extensive!

The Geometry of Music
Excellent article that takes a more mathematical approach to music analysis.

Blog: Ideas and thoughts on computer programs that compose music
Katarina Miljkovic seems to have produced a new electronic piece of music for her blog each day for quite some time in 2007. I haven't yet figured out if these pieces were generated algorithmically or by her, but it seems like a valuable resource.

Music Algorithms
Really cool website dedicated to demonstrating the mathematical relations in music. It has a bunch of fun tools that let you try algorithmic composition yourself!

The Voice of Al-Kwarismi
Great list of sources related to algorithms, music, and math in general.

Generative Art and Music
Yet another helpful list of sources related to generative music.

Intermorphic

I discovered the company Intermorphic. They market three algorithmic composition tools: a generative music application for desktops, one for mobile devices, and a generative lyrics tool. The programs are known as noatikl, mixtikl, and liptikl, respectively. Brian Eno, known of the father of ambient music, helped author the programs.

Considering that these make up a large portion of the algorithmic composition software that I have encountered so far, I think it would be wise for me to study them and learn how they operate. I have messed around briefly with noatikl already, and have found the following:

Pros of Noatikl

• Clean interface
• Well-structured generative process
• Flexible, powerful scripting engine and ability to make "hyperinstruments"

Cons of Noatikl

• While the interface is clean, it's not altogether inviting to inexperienced users
• Not powerful enough for genres outside of ambient

In short, Noatikl is a fairly well-implemented algorithmic composition program, but it lacks the power to break outside of the droning ambient genre, thus severely hindering its mass appeal.

Neural Networks - Emulating Human Creativity

Upon finding the article Algorithmic Composition and Reductionist Analysis: Can a Machine Compose? I immediately got excited. The author gives a great overview of algorithmic composition and details his own endeavors into the field.

In particular, the author touches on the concept of heuristic algorithms - including both genetic algorithms and neural networks - that slowly approach a desired solution by having a human evaluator determine the fitness of the system. I found the following quote astounding:

"A researcher trained a neural network to recognize makes of car from a photograph, and he decided to look inside the network at the individual neurons, rather than regarding it as a "black box" that somehow worked for some incomprehensible reason. He found that certain areas of the network were specializing into recognizing certain features of the car, and, by introducing a level of random "noise" into the network, got the network to design its own cars."

And finally, the author's take on algorithmic composition and creativity:

"I have always felt very uneasy about throwing any musical ideas away, as it would amount to destroying something that I think is unique. But, if computer composition took over to a degree, would the 'preservation people' be content with the idea that the music exists, somewhere, within the set of possibilities? May I delete Clara Empricost's symphony with impunity, once it has generated it? Should I preserve the algorithm and the random number seeds somewhere? An interesting set of problems."

GenJam

Found a program today by the name of GenJam. It makes use of evolutionary genetic algorithms to "learn" to play jazz by listening to a human play trumpet. The program is private, but there a few links to sample clips in which you can hear GenJam playing along with the human.

GenJam

Two Research Questions

Question: can algorithmic composition be used to compose a creative, coherent, and above-all good-sounding score?
Key Words: algorithmic composition; genetic algorithms; fractal algorithms;
Strategies: bring together creative algorithms and modules of varying types within an overarching framework to create coherency; use variation and stochastic processes to create creativity; use expert systems (heavily coded with music theory) to make sure it sounds good

Genetic composition algorithms; talks specifically about designing an “ear module” that can filter the search space of the solution to a good composition
http://www.ece.umd.edu/~blj/algorithmic_composition/icmc.95.html

Resources for algorithmic composition
http://www.flexatone.net/algoNet/

Question: can a human eye and the intelligence to interpret its output evolve within a bounded timeframe (3.8 billion years or less) from a simple patch of photoreceptor cells?
Key Words: evolution; natural selection; Darwin; irreducible complexity; intelligent design