Weather Analytics is constantly striving to find new uses for our data, new tools to integrate into our array, and (most importantly) new ways to make our data accessible to the world at large.
It only seemed natural then that we get creative and use data to make a song. This is that story.
Data sonification isn’t new, but it’s not remarkably well known either. CERN has made symphonies from data (http://home.web.cern.ch/about/updates/2014/10/cern-scientists-perform-their-data) and Black Midi has gotten into the game as well.
For our song, we wanted to start with something simple, someplace close to home.
What follows is the process of how we turned 48 hours of our Washington DC area weather data (From Jan 1st & 2nd, 2014) into music.
Weather Analytics’ standard dataset is broken into 14 standard variables, which includes things like windspeed, rainfall, direct normal sunlight, temperature, etc. These became our notes.
The biggest trouble was finding the right software to make everything we imagined work to create the song utilizing these notes. We tried x, y, and z, before creating a MIDI file and translating it into a score with MuseScore.
Hear the final song here:
(Prior to using our own data we started the project by trying to make music from the data behind satellite imagery. The data behind the cloud cover amounts for this satellite image in the picture below was translated into notes but the sheer amount of data was difficult to process into a song).
Read the full documentation by our data intern, Hannah, here:
I started by downloading xSonify. I initially couldn’t import documents into xSonify, and couldn’t figure out why. Later on, I figured out that the difficulty was that there were specific instructions needed to be followed on the NASA website, but by that time, an update from Java had changed its security level so that xSonify wouldn’t even open. So that option didn’t work.
Next, I tried Sonification Sandbox, which consists mainly of a spreadsheet and sound editing tools.
I started out by replacing the two lines of numbers with two lines of data from my Excel sheet, and that made an interesting tune. When I put in more lines, however, the document froze, and then wouldn’t make any music whatsoever. Later on, I realized I had been doing things wrong—the first line of numbers should stay the same because it marked time. I fixed that, but I still had trouble with trying to copy more lines of data in. I tried importing the numbers in but that didn’t work either. At this point, I was only importing 40 instances of each weather variable in at a time.
Then I tried using Audacity and importing it in that way. This time I imported everything I could, including all 8,000-some instances of each weather variable. Audacity ended up producing more noise than music. Some of it was very interesting noise, such as the wind noise I ended up producing at one point—but most of it was just halting to the ears. Eventually, I gave up and went back to Sonification Sandbox.
This time, instead of trying to put all the variables in at once, I did them one at a time (60 instances of each), and then exported each one into a midi file. I downloaded a program called Aria Maestosa, into which I imported the Midi files and played them together. I tried to pick the most musical ones and make sure they blended. I also changed the key and the tones for each of the sounds to help the music blend.
Finally, I exported the result as a MIDI file, and used another software, MuseScore, to convert the final MIDI file into a score. An example of one of the pieces of sheet music generated can be seen below.
Overall, this was a fun experiment with our weather data to use sonification to put music to a couple of cold days in January with mild winds.