Music 220b: Winter 2001
Fernando Lopez-Lezcano, instructor
Christopher Burns, teaching assistant
Tamara Smyth, teaching assistant

Week 5: composing with large datasets

The issue of "data explosion" has come up a number of times. Computer music often requires huge amounts of data, especially when a composer uses parameter-intensive synthesis techniques like additive or granular synthesis. Algorithmic generation of parameters is one way of dealing with this issue, but there are others. For instance, there's plenty of data out in the world -- everything from U.S. Census counts to weather statistics to the yellow pages. Why not use it?

Well, it's a bit more complicated than that. There are at least two essential questions: what are the trends in the data that can be exploited to musical effect? And how do we map the data to our parameters, to get that effect? Or, to put it another way, how do we choose the data, and then how do we use it?

Trend analysis of a dataset can be incredibly complex, but we don't have to be statisticians to make it possible. For a simple example, think of the Dow Jones average -- on any given day it may rise or fall, but considered over the last seventy years it's risen substantially. If we wanted some kind of trend in our music which was highly variable on the local scale, and generally increasing on the large scale, we could choose a worse dataset. For a real-world example, consider Charles Dodge's Earth's Magnetic Field -- a composition based on, you guessed it.

For a case study, we'll consider soundfiles as datasets... after all, we've got a few of those around. And what is a soundfile but a string of numbers? (Well, those numbers have some interesting properties when sonified....) In particular we'll use speech soundfiles -- which you could (reductively) think of as a noisy oscillation between -1 and 1, with occasional stretches of near-zero between the words.

As always, there are countless ways to approach composition with datasets. Let us know what you come up with....

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