Dr Rebecca Fiebrink
Title
Professor - Research
College
University of the Arts London
Email address
Tags
Researcher Research
Biography
I am a Reader at the UAL Creative Computing Institute. My students, research assistants, and I work on a variety of projects developing new technologies to enable new forms of human expression, creativity, and embodied interaction. Much of my current research combines techniques from human-computer interaction, machine learning, and signal processing to allow people to apply machine learning more effectively to new problems, such as the design of new digital musical instruments and gestural interfaces for gaming and accessibility. I am also involved in projects developing rich interactive technologies for digital humanities scholarship, and machine learning education. I am the creator of the Wekinator (http://www.wekinator.org/) tool for real-time interactive machine learning and teach the Machine Learning for Musicians and Artists course on Kadenze (https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info).External Links
Research Outputs
Article
- Bernardo F, Zbyszynski M, Grierson M, Fiebrink R. Designing and Evaluating the Usability of a Machine Learning API for Rapid Prototyping Music Technology (2020)
- Fiebrink R. Machine Learning Education for Artists, Musicians, and Other Creative Practitioners (2019)
- Jeon M, Fiebrink R, Edmonds EA, Herath D. From rituals to magic: Interactive art and HCI of the past, present, and future (2019)
Book Section
- Holland S, Fiebrink R. Machine Learning, Music and Creativity: An Interview with Rebecca Fiebrink (2019)
Conference, Symposium or Workshop item
- Farkas T, Denisova A, Wiseman S, Fiebrink R. The Effects of a Soundtrack on Board Game Player Experience (2022)
- Niehaus KH, Fiebrink R. Making Up 3D Bodies: Artistic and Serendipitous Modeling of Digital Human Figures (2021)
- Djokic VG, Shutova E, Fiebrink R. MetaVR: Understanding metaphors in the mind and relation to emotion through immersive, spatial interaction (2021)
- Farkas T, Wiseman S, Cairns P, Fiebrink R. A Grounded Analysis of Player-Described Board Game Immersion (2020)
- Plant N, Zbyszynski M, Gonzalez Diaz C, Hilton C, Fiebrink R, Gibson R, Gillies M, Perry P, Martelli B. Programming by Moving: Interactive Machine Learning for Embodied Interaction Design (2020)
- McCallum L, Fiebrink R. The challenge of feature engineering in programming for moving bodies (2020)
- Hilton C, Gonzalez Diaz C, Gibson R, Perry P, Fiebrink R, Zbyszynski M, Plant N, Martelli B, Gillies M. InteractML: Node Based Tool to Empower Artists and Dancers in using Interactive Machine Learning for Designing Movement Interaction (2020)
- Vigliensoni G, McCallum L, Maestre E, Fiebrink R. Generation and visualization of rhythmic latent spaces (2020)
- Vigliensoni G, McCallum L, Fiebrink R. Creating Latent Spaces for Modern Music Genre Rhythms Using Minimal Training Data (2020)
- Fiebrink R, Sonami L. Reflections on Eight Years of Instrument Creation with Machine Learning (2020)
- Plant N, Gibson R, Gonzalez Diaz C, Martelli B, Zbyszynski M, Fiebrink R, Gillies M, Hilton C, Perry P. Movement interaction design for immersive media using interactive machine learning (2020)
- Gonzalez Diaz C, Perry P, Fiebrink R. Interactive Machine Learning for More Expressive Game Interactions (2019)
- Parke-Wolfe ST, Scurto H, Fiebrink R. Sound Control: Supporting Custom Musical Interface Design for Children with Disabilities (2019)
- McCallum L, Fiebrink R. Supporting Feature Engineering in End-User Machine Learning (2019)
- Ramos G, Suh J, Ghorashi S, Meek C, Banks R, Amershi S, Fiebrink R, Smith-Renner A, Bansal G. Emerging Perspectives in Human-Centered Machine Learning (2019)
- Fiebrink R, Cook PR, Trueman D. Human Model Evaluation in Interactive Supervised Learning (2011)
- Fiebrink R, Trueman D, Cook PR. A Meta-Instrument for Interactive, On-the-fly Machine Learning (2009)
Subjects
Animation, interactive, film and sound
Creative Computing