Deep Learning and Music

The last five years have seen incredible results from machine learning using modern neural networks, also called Deep Learning. Some of this research has presented completely new ways of working with digital media including sound and images. I will provide an informal survey of new approaches to music composition and sound synthesis based on neural networks, including some of my own work in this direction.

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Kyle McDonald is an artist working with code. He is a contributor to open source arts-engineering toolkits like openFrameworks, and builds tools that allow artists to use new algorithms in creative ways. He has a habit of sharing ideas and projects in public before they're completed. He creatively subverts networked communication and computation, explores glitch and systemic bias, and extends these concepts to reversal of everything from identity to relationships. Kyle has been an adjunct professor at NYU's ITP, and a member of F.A.T. Lab, community manager for openFrameworks, and artist in residence at STUDIO for Creative Inquiry at Carnegie Mellon, as well as YCAM in Japan. His work is commissioned by and shown at exhibitions and festivals around the world, including: NTT ICC, Ars Electronica, Sonar/OFFF, Eyebeam, Anyang Public Art Project, Cinekid, CLICK Festival, NODE Festival, and many others. He frequently leads workshops exploring computer vision and interaction.

http://kylemcdonald.net/