Robotic Musicianship Group
The Robotic Musicianship Group aims to facilitate meaningful musical interactions between humans and machines, leading to novel musical experiences and outcomes. In our research we combine computational modeling approaches for perception, interaction, and improvisation, with novel approaches for generating acoustic responses in physical and visual manners. The motivation for this work is based on the hypothesis that real-time collaboration between human and robotic players can capitalize on the combination of their unique strengths to produce new and compelling music. Our goal is to combine human qualities such musical expression and emotions with robotic traits such as powerful processing, the ability to perform sophisticated mathematical transformations, robust long-term memory, and the capacity to play accurately without practice.
Faculty: Gil Weinberg, Andrea Thomaz
PostDocs: Guy Hoffman
Students: Brian Blosser, Trishul Mallikarjuna, Aparna Raman, Vamsi Bharadwaj, Maya Cakmak, Crystal Chao, Michael Gielniak, Brian Sherwell
Alumni: Scott Driscoll
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