English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Although deep learning has revolutionized music generation, existing methodsfor structured melody generation follow an end-to-end left-to-rightnote-by-note generative paradigm and treat each note equally. Here, we presentWuYun, a knowledge-enhanced deep learning architecture for improving thestructure of generated melodies, which first generates the most structurallyimportant notes to construct a melodic skeleton and subsequently infills itwith dynamically decorative notes into a full-fledged melody. Specifically, weuse music domain knowledge to extract melodic skeletons and employ sequencelearning to reconstruct them, which serve as additional knowledge to provideauxiliary guidance for the melody generation process. We demonstrate that WuYuncan generate melodies with better long-term structure and musicality andoutperforms other state-of-the-art methods by 0.51 on average on all subjectiveevaluation metrics. Our study provides a multidisciplinary lens to designmelodic hierarchical structures and bridge the gap between data-driven andknowledge-based approaches for numerous music generation tasks.

WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning