Listening to Data - Sonification Using Open Source Tools

Accepted Session
Short Form
Scheduled: Wednesday, June 2, 2010 from 2:30 – 3:15pm in Fremont


Hearing your data - exploratory data analysis by way of algorithmic composition


Data visualization – telling stories about data using graphics and animation – is a popular technique, and new tools, both proprietary and open source, appear almost daily, as do huge datasets to analyze.

But there’s another way to explore large datasets – by mapping the properties of data points into music. This technique is called sonification, and adds another interesting dimension to exploratory data analysis.

In this presentation, I’ll briefly describe the main techniques of sonification, then present some examples where sonification was key in understanding the story behind the data. And, of course, you’ll get to hear the music that’s in the data.

Speaking experience


  • Mugshot


    Media Inactivist, Thought Follower, Sit-Down Comic, Social Media Analytics Researcher, Former Boy Genius, Linux Capacity Planner, R Hacker, Mathematician

    “M. Edward (Ed) Borasky is, in order of appearance, a boy genius, computer programmer, applied mathematician, folk singer, actor, professional graduate student, armchair astronaut, algorithmic composer, supercomputer programmer, performance engineer, Linux geek, solution in search of a problem and Social Media Non-Guru. His hobby is collecting hobbies.”

    I’ve been on the Internet a long time. I had my first personal web site in 1994, and registered my first domain,, in 2001. And I’ve got a lot of interests:

    • Twitter – the phenomenon, the tools, the data, the global cocktail party conversation
    • Social Media Analytics Research
    • Linux Capacity Planning, Server Profiling and Performance Troubleshooting
    • Algorithmic Composition and Synthesis of Music
    • Computational Finance

    and the underlying technologies: programming languages, applied mathematics and statistics, artificial intelligence, digital signal processing, machine learning, software engineering and high-performance computing. About the only technology I’m not really into is hardware.