Subscription id: 22499
Course id: 2203393
Course product id: 2355861
Subscription end date: 01/01/1970-12:01:00
Next payment date: 04/04/2023-02:04:50
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Not logged in, cannot sync.De-Mix Audio using FluCoMa in Max
Taught by: Ted Moore
Sunday 26th March 4pm UK, 5pm Berlin, 8am LA, 11am NYC
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
Level
Starts on: 26/03/2023 16:00 London time
Tribe start time
2023-03-26 16:00:00What you'll learn
- De-mix recorded audio into sound objects
- Deconstruct real-time audio streams
- Identify important sounds in real-time audio
- Seed this process to help the algorithm find the sound objects we're interested in
Course content
- Open the course in Zoom to access all the features
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Add to cartRequirements
- A working PC, Laptop, iMac or MacBook
- Max software
- Download the FluCoMa Package from the Package Manager
- A Zoom account
- A mic and camera is recommended, but not necessary
- It is suggested to first watch the free course Using Machine Learning Creatively via FluCoMa in Max
Who is this course for
- Sound Designers searching for new ways of combining sounds
- Audio Engineers intrigued by de-mixing possibilities
- Creative artists interested in machine learning algorithms for music making
Useful links
About the workshop leader
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.
