Machine Learning, Music and Creativity: An Interview with Rebecca Fiebrink

  • Simon HollandEmail author
  • Rebecca Fiebrink
Part of the Springer Series on Cultural Computing book series (SSCC)


Rebecca Fiebrink is a Senior Lecturer at Goldsmiths, University of London, where she designs new ways for humans to interact with computers in creative practice. As a computer scientist and musician, much of her work focuses on applications of machine learning to music, addressing research questions such as: ‘How can machine learning algorithms help people to create new musical instruments and interactions?’ and ‘How does machine learning change the type of musical systems that can be created, the creative relationships between people and technology, and the set of people who can create new technologies?’ Much of Fiebrink’s work is also driven by a belief in the importance of inclusion, participation, and accessibility. She frequently uses participatory design processes, and she is currently involved in creating new accessible technologies with people with disabilities, designing inclusive machine learning curricula and tools, and applying participatory design methodologies in the digital humanities. Fiebrink is the developer of the Wekinator: open-source software for real-time interactive machine learning, whose current version has been downloaded over 10,000 times. She is the creator of a MOOC titled “Machine Learning for Artists and Musicians.” She was previously an Assistant Professor at Princeton University, where she co-directed the Princeton Laptop Orchestra. She has worked with companies including Microsoft Research, Sun Microsystems Research Labs, Imagine Research, and Smule. She has performed with a variety of musical ensembles playing flute, keyboard, and laptop. She holds a Ph.D. in Computer Science from Princeton University.


  1. Fiebrink R (2011) Real-time human interaction with supervised learning algorithms for music composition and performance. PhD thesis, Princeton UniversityGoogle Scholar
  2. Fiebrink R, Trueman D, Cook PR (2009) A meta-instrument for interactive, on-the-fly machine learning. In: Proceedings of new interfaces for musical expression (NIME), Pittsburgh, 4–6 JuneGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Music Computing Lab, Centre for Research in ComputingThe Open UniversityMilton KeynesUK
  2. 2.Department of Computing, GoldsmithsUniversity of LondonLondonUK

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