Brainstorming

• reviewing the dataset
• locality using Hilbert curves
• avoid visual thinking -> what would be better auditory thinking
• whole image into sound object (spins->parameters: data to parameters of sound object
• do not fix on one objects, be aware of many many objects
• serialization between many objects
• serialization is done via time
• matrix as stationary sound
• statistical stuff not that interesting
• matrix as orchestra from above
• neighborhood should be preserved
• avoid choices
• taking some scanning lines
• straight line through plot
• we are interested in emergent properties
• envelope generation via several lines, counting how far apart we go until we have 3, 6, 9, 12 spin flips.
• what is a cluster: a region of similar spin alignment
• relation to cellular automata...
• random sampling and local shape into sound
• what do we gain if we loose locality: we gain that the system cares less to locality than our eyes do. we loose structure of area around grain. (refocus...)
• local shape is more or less correlation function
• lets start with simple approach

Results

We sample 5 positions per frame, creating one grain event for each .

At each position we compute average magnetism over a qxq patch

We perform nonlinear mapping of these values to grain properties, namely

• value -> pitch
• amplitude -> deviation from 0.5
• noise -> if mean around 0.5

In the sonification a sequence of frames is played, from above MC-Temp to below MC-Temp.

Sound Example: around the phase transition the number of noise bursts decreases and the purity and loudness of clean tones increases.