Computers and musical style
ACM Computing Surveys (CSUR)
Computer Music: Synthesis, Composition and Performance
Computer Music: Synthesis, Composition and Performance
Learning physics-based motion style with nonlinear inverse optimization
ACM SIGGRAPH 2005 Papers
Music compositional intelligence with an affective flavor
Proceedings of the 12th international conference on Intelligent user interfaces
MySong: automatic accompaniment generation for vocal melodies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exposing parameters of a trained dynamic model for interactive music creation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
MelodicBrush: a cross-modal link between ancient and digital art forms
CHI '12 Extended Abstracts on Human Factors in Computing Systems
MelodicBrush: a novel system for cross-modal digital art creation linking calligraphy and music
Proceedings of the Designing Interactive Systems Conference
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We present data-driven methods for supporting musical creativity by capturing the statistics of a musical database. Specifically, we introduce a system that supports users in exploring the high-dimensional space of musical chord sequences by parameterizing the variation among chord sequences in popular music. We provide a novel user interface that exposes these learned parameters as control axes, and we propose two automatic approaches for defining these axes. One approach is based on a novel clustering procedure, the other on principal components analysis. A user study compares our approaches for defining control axes both to each other and to an approach based on manually-assigned genre labels. Results show that our automatic methods for defining control axes provide a subjectively better user experience than axes based on manual genre labeling.