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- W3141854951 abstract "We present SyVMO, an algorithmic extension of the Variable Markov Oracle algorithm, to model and predict multi-part dependencies from symbolic music manifestations. Our model has been implemented as a software application named INCITe for computer-assisted algorithmic composition. It learns variable amounts of musical data from style-agnostic music represented as multiple viewpoints. To evaluate the SyVMO model within INCITe, we adopted the Creative Support Index survey and semi-structured interviews. Four expert composers participated in the evaluation using both personal and exogenous music corpus of variable size. The results suggest that INCITe shows great potential to support creative music tasks, namely in assisting the composition process. The use of SyVMO allowed the creation of polyphonic music suggestions from style-agnostic sources while maintaining a coherent melodic structure." @default.
- W3141854951 created "2021-04-13" @default.
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- W3141854951 date "2021-01-01" @default.
- W3141854951 modified "2023-09-24" @default.
- W3141854951 title "SyVMO: Synchronous Variable Markov Oracle for Modeling and Predicting Multi-part Musical Structures" @default.
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- W3141854951 doi "https://doi.org/10.1007/978-3-030-72914-1_3" @default.
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