  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
description-to-sequence task, which allows for fine-grained controllable
generation on a global level. We do so 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
modelling setup. We train FIGARO (FIne-grained music Generation via
Attention-based, RObust control) by applying description-to-sequence modelling
to symbolic music. By combining learned high level features with domain
knowledge, which acts as a strong inductive bias, the model achieves
state-of-the-art results in controllable symbolic music generation and
generalizes well beyond the training distribution.
