Scientist in the Spotlight:
Converting Data into Music
Mark Ballora, professor of music technology at Pennsylvania State University, is changing the way researchers interpret scientific data by transforming datasets into musical scores. Ballora has worked with former Grateful Dead percussionist and ethnomusicologist
Mickey Heart and Nobel Laureate George Smoot on the DVD project “Rhythms of the Universe.”
More recently, Ballora received two seed grants from the National Academies Keck Futures Initiative
(NAKFI) and the Gulf Research Program to create sonifications in the area of ocean research.
Managing Editor Lauren Scrudato spoke with Ballora to learn more about the field of sonification
and how it can offer a new perspective on analyzing research results.
LS: Can you tell me about your career in music technology and the type of
work you conduct at Penn State?
MB: After collecting tape decks, synthe- sizers and personal computer gear in
the 1980s, I went to grad school in the 1990s
to study music technology and then composi-
tion at NYU, then for a Ph.D. in computer music
applications at McGill. At Penn State, my job
was to set up a music technology program for
students in the School of Music. We’ve now got
a minor in music technology, and started a BA
in music technology in 2015. I teach courses in
musical acoustics, basics of music production
with a laptop, history of electroacoustic music,
and software programming for music.
LS: Can you walk through the steps of transforming datasets into music?
MB: I might start in a spreadsheet program and just explore the dataset a bit, plot-ting it in various ways, just to get an idea of its
behavior, value range, and so forth. From there,
I’ll export it as a .csv or space delimited file and
bring it into SuperCollider, an audio synthesis
program that is well suited for sonification work.
Then it’s a matter of designing an instrument
to play the dataset like a musical score, and
stepping through the dataset, applying its values
to the instrument so that they change its charac-
teristics such as pitch, vibrato rate, volume, pan
position, and so on.
The fun part is in coming up with a sound
that can illustrate the data’s behavior effective-ly, and that makes sense on an intuitive level.
This varies, depending on the type of data. In
a recent example, [I was working with] pulsar
data. The dataset was the pulsation cycle of a
number of electromagnetic frequencies. So it
was a matter of transposing these frequencies
down many octaves so that they corresponded
to audible sound frequencies, then creating a
sine wave oscillator that played each of these
frequencies, with loudness levels that followed
Mark Ballora, Penn State. Photo:
Patrick Mansell/Penn State
IN THE SPOTLIGHT