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Magenta Studio: Augmenting Creativity with Deep Learning in Ableton Live
Yotam Mann
Jon Gillick
Monica Dinculescu
Carey Radebaugh
Curtis Hawthorne
Proceedings of the International Workshop on Musical Metacreation (MUME) (2019)
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The field of Musical Metacreation (MuMe) has pro-duced impressive results for both autonomous and in-teractive creativity. However, there are few examplesof these systems crossing over to the “mainstream” ofmusic creation and consumption. We tie together ex-isting frameworks (Electron, TensorFlow.js, and MaxFor Live) to develop a system whose purpose is tobring the promise of interactive MuMe to the realmof professional music creators. Combining compellingapplications of deep learning based music generationwith a focus on ease of installation and use in a pop-ular DAW, we hope to expose more musicians and pro-ducers to the potential of using such systems in theircreative workflows. Our suite of plug-ins for AbletonLive, named Magenta Studio, is available for downloadathttp://g.co/magenta/studioalong with itsopen source implementation.
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Identifying the intersections: User experience + research scientist collaboration in a generative machine learning interface
Jess Scon Holbrook
ACM CHI Conference 2019 (2019)
Preview abstract
Creative generative machine learning interfaces are stronger when multiple actors bearing different points of view actively contribute to them. User experience (UX) research and design involvement in the creation of machine learning (ML) models help ML research scientists to more effectively identify human needs that ML models will fulfill. The People and AI Research (PAIR) group within Google developed a novel program method in which UXers are embedded into an ML research group for three months to provide a human-centered perspective on the creation of ML models. The first full-time cohort of UXers were embedded in a team of ML research scientists focused on deep generative models to assist in music composition. Here, we discuss the structure and goals of the program, challenges we faced during execution, and insights gained as a result of the process. We offer practical suggestions for how to foster communication between UX and ML research teams and recommended UX design processes for building creative generative machine learning interfaces.
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