Sonic and Visual Representation of Data

This course explores the creative visualization and sonification of data. Humans produce enormous amounts of data representing complex phenomena (including but not limited to our own activities), but we there is a deficit in our ability to perceive and understand the patterns in the data. The auditory and visual perceptual systems are optimized for a wide range of spatial and temporal patterns that we process simultaneously to understand our immediate surroundings. How can we use these capabilities to better understand processes that are beyond the range of our direct perception, but we can measure indirectly with a vast range of sensors? This course addresses ways of generating both sonic and visual animations of the same data, from which we will construct videos. Questions of how to design and tune these representations to bring out patterns in the data, based on the nature of human perception and also aesthetic choices, will be discussed throughout. How might these questions of pattern perception vary (or not) for scientific and artistic intents? Students will select datasets they are interested in early in the course, and will develop and build these projects over the semester. While the course is taught using Python and RTcmix, and prior experience in Python is encouraged, students may use other sonic/visual coding environments such as Unity, Max/MSP, or Pure Data for their projects. Hardware for VR/AR and spatialized sound will be available for class use at the Computer Music Center.