WuYun：探索使用知识增强的深度学习的分层旋律引导旋律生成
WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning
摘要：
尽管深度学习已经彻底改变了音乐生成，但是用于结构化旋律生成的现有方法遵循端到端、从左到右的逐音符生成范例，并且平等地对待每个音符。在这里，我们提出了WuYun，一个知识增强的深度学习架构，用于改善生成的旋律的结构，它首先生成结构上最重要的音符来构建旋律骨架，然后用动态装饰音符填充到完整的旋律中。具体来说，我们使用音乐领域的知识提取旋律骨架，并采用序列学习来重建它们，作为额外的知识，为旋律生成过程提供辅助指导。我们证明了WuYun可以生成具有更好的长期结构和音乐性的旋律，并且在所有主观评价指标上平均优于其他最先进的方法0.51。我们的研究提供了一个多学科的镜头来设计旋律的层次结构，并弥合数据驱动和基于知识的方法之间的差距，为众多的音乐生成任务。
英文摘要：
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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