FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control. (arXiv:2201.10936v1 [cs.SD])

Web: http://arxiv.org/abs/2201.10936

Generating music with deep neural networks has been an area of active
research in recent years. While the quality of generated samples has been
steadily increasing, most methods are only able to exert minimal control over
the generated sequence, if any. We propose the self-supervised
\emph{description-to-sequence} task, which allows for fine-grained controllable
generation on a global level by extracting high-level features about the target
sequence and learning the conditional distribution of sequences given the
corresponding high-level description in a sequence-to-sequence …

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