Loud Numbers is a data sonification podcast, created by Duncan Geere and Miriam Quick. Data sonification is the process of turning data into sound, and we take it a step further by turning those sounds into music. In each episode, we introduce a data story, explain how we sonified it, and then play the sonification we’ve created.
You can’t yet. The first season of Loud Numbers will be released on 5 June 2021. In the meantime, you might be able to get a sense of what it’s going to sound like through our development log newsletter.
Miriam Quick is a data journalist, researcher and author who explores novel ways of communicating data. She co-creates artworks that represent data through images, sculpture and sound and has a PhD in music from King’s College London. Her first book, I am a book. I am a portal to the universe., co-authored with Stefanie Posavec, was published in September 2020.
Duncan Geere is an information designer based in Gothenburg, Sweden, interested in climate and the environment. He works to communicate complex, nuanced information to a wider audience for a wide range of clients. He also works part-time for the climate charity Possible, and he's a generative artist and musician.
We’ve tried a lot of sonification tools, and settled on a core technology stack of Google Sheets into Sonic Pi into Logic Pro. We normally do our calculations in Google Sheets, then export the data in .csv format. We then import the .csv to Sonic Pi and code the sonifications there. Finally, we export the sonifications as .wav files, and import them to Logic Pro to turn them into a song. Occasionally, we create simple sonification layers directly in Logic.
Sonification is a fantastic accessibility tool - especially when it’s combined with visualization. There are lots of fantastic sonification works with a focus on accessibility, like Hear The Blind Spot and the simulations created by PhET at the University of Colorado Boulder.
We created Loud Numbers because we both love music and wanted to experiment with the creative possibilities of sonification. While we’re really excited about the project allowing a wider audience to enjoy data storytelling, we didn’t intentionally design it for accessibility reasons.
Sonification is still an immature field, and there aren’t really “standards” yet. Data to pitch is a very common sonification mapping – the higher the pitch, the larger the quantity – but like many pie charts it’s not always executed very well. There are many other options, such as data to loudness or data to instrument. But like visualization, how you map data to sound depends so much on your data, the story and how your audience will experience it!
Definitely, but sonification is much less common so it’ll probably take longer for those guidelines and standards to emerge. We’re interested in publishing a “Loud Numbers sonification style guide” of some sort at some point. But we need to get the podcast finished first!
Great question. There’s a whole fascinating grey area between music driven by data, music that encodes texts (like musical cryptograms), and music that’s structured according to quasi-mathematical principles. . When you map data to audio or musical parameters, the data does need to be numeric or categorical, but you can of course convert text to numbers and work with it that way. Or you can treat text as a kind of “loose” data - like we’re doing in our Boom & Bust episode, where historical samples are positioned along a musical timeline.
Depends on your goal! If you’re making a sonification to communicate information then it’s important that people understand it. In this situation, you might find it tempting to create a visual legend or accompanying video – but this can sometimes draw focus away from the sound, as people are much more familiar with visualization than sonification.
We’re interested in experimenting with what happens when you can’t rely on visual backup. The introduction to each episode of Loud Numbers explains how the sonification works with some examples, which is kind of like a legend but in audio.
If all you’re interested in is creating something that sounds nice, and it’s not important whether people can pull data out of it or not, then a legend (audio or otherwise) is probably unnecessary.
Quite a lot! But that’s a personal choice, because we’re interested in how musical and data structures interact (or don’t). Miriam has a PhD in music, but Duncan has no formal training. You definitely don’t need a music theory background. Make punk sonifications!
Probably! It’ll be much easier to organise if you can give us a bit of notice.
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