FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control
This addresses the need for more controllable music generation tools for musicians and creators, representing a novel method rather than an incremental improvement.
The paper tackles the problem of limited control in neural network-based symbolic music generation by introducing a self-supervised description-to-sequence task, achieving state-of-the-art results with fine-grained artistic control and strong generalization beyond the training distribution.
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.