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- W2943532091 abstract "The quality of outputs produced by deep generative models for music have seen a dramatic improvement in the last few years. However, most deep learning models perform in offline mode, with few restrictions on the processing time. Integrating these types of models into a live structured performance poses a challenge because of the necessity to respect the beat and harmony. Further, these deep models tend to be agnostic to the style of a performer, which often renders them impractical for live performance. In this paper we propose a system which enables the integration of out-of-the-box generative models by leveraging the musician's creativity and expertise." @default.
- W2943532091 created "2019-05-09" @default.
- W2943532091 creator A5068291173 @default.
- W2943532091 date "2019-04-30" @default.
- W2943532091 modified "2023-09-27" @default.
- W2943532091 title "Performing Structured Improvisations with pre-trained Deep Learning Models" @default.
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