Professor: Douglas Repetto, douglas@music.columbia.edu
TA: Johnathan Lee, jlee@music.columbia.edu
Our Motto: "Why, then how."
We'll start off with the research presentations we didn't get to last
week. Then, since we all spent so much time last week deriding most of the
algorithmic works in your research presentations, we'll take some time to
listen to and talk about a bunch of successful algorithmic (in a broad
sense) pieces. These will include:
I Am Sitting in a Room Alvin LucierFinally, we'll leave the "What Is an Algorithm?" question and continue with its corolarry: "Now That I've Got All This Data, What Do I Do with It?", AKA:
Bedhaya Guthrie/Bedhaya Sadra Larry Polansky
51 Melodies Larry Polansky
Four Voice Cannon #12: Doggerel (from 3 New Hampshire Songs) Larry Polansky
In Sara, Mencken, Christ and Beethoven there were men and women Robert Ashley
Movimento preciso e meccanico from Kammerkonzert fur 13 instrumentalisten Gyorgy Ligeti
Data is malleable, particularly when it's in digital form. When a scientist conducts an experiment, or a researcher does a survey, or a sensor takes a reading, there's always an interpretive step between the collection of the data and its presentation. That interpretive step is a crucial part of the process that makes raw data intelligible to human observers, and involves (hopefully) carefully thought out technical and aesthetic choices made by the data gatherer. It's often also a controversial step, as it's the place where the data gatherer's biases most easily (and often unconciously) shape the ways in which the data is presented.
Artists also regularly deal with issues of data interpretation (even if they don't think of it in that way), and the process of mapping information from one domain into another presents many interesting questions and challenges. Over the next few weeks we'll explore some of the many ways of mapping data between various domains such as: sound, image, movement, real world sensors, algorithmic output, historical records, text files, and so on. Fun stuff!
Your assignment:
1) Take a piece you've created in the past and recontextualize it in terms
of The Mapping Problem.
2) Piece Proposal: Take a data set. Map it. What? Why? How?