Abstract
Although deep learning has revolutionized music generation, existing methods
for structured melody generation follow an end-to-end left-to-right
note-by-note generative paradigm and treat each note equally. Here, we present
WuYun, a knowledge-enhanced deep learning architecture for improving the
structure of generated melodies, which first generates the most structurally
important notes to construct a melodic skeleton and subsequently infills it
with dynamically decorative notes into a full-fledged melody. Specifically, we
use music domain knowledge to extract melodic skeletons and employ sequence
learning to reconstruct them, which serve as additional knowledge to provide
auxiliary guidance for the melody generation process. We demonstrate that WuYun
can generate melodies with better long-term structure and musicality and
outperforms other state-of-the-art methods by 0.51 on average on all subjective
evaluation metrics. Our study provides a multidisciplinary lens to design
melodic hierarchical structures and bridge the gap between data-driven and
knowledge-based approaches for numerous music generation tasks.
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