The first time I heard an example of what this algorithm can do, I was super impressed. It broke apart a mono drum loop into three tracks. One had only the snare sounds, one had only the kick drum sounds, and one had only the cymbal sounds. This sparked a lot of thinking about how I could use this for creativity. Then I found out it also can break apart and identify specific sounds in real-time audio. In this workshop I'll show you how these tools work in the FluCoMa toolkit and some of the ways I've been using it in my own work.
It's suggested that you have completed the FREE course: Using Machine Learning Creatively via FluCoMa in Max
Ted Moore (he / him) is a composer, improviser, and intermedia artist. He holds a PhD in Music Composition from the University of Chicago and recently served as a Research Fellow in Creative Coding at the University of Huddersfield, investigating the creative affordances of machine learning and data science algorithms as part of the FluCoMa project. His work focuses on fusing the sonic, visual, physical, and acoustic aspects of performance and sound, often through the integration of technology.
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