Lyric-to-melody generation is an important task in songwriting, and is also
quite challenging due to its unique characteristics: the generated melodies
should not only follow good musical patterns, but also align with features in
lyrics such as rhythms and structures. These characteristics cannot be well
handled by neural generation models that learn lyric-to-melody mapping in an
end-to-end way, due to several issues: (1) lack of aligned lyric-melody
training data to sufficiently learn lyric-melody feature alignment; (2) lack of
controllability in generation to better and explicitly align the lyric-melody
features. In this paper, we propose Re-creation of Creations (ROC), a new
paradigm for lyric-to-melody generation. ROC generates melodies according to
given lyrics and also conditions on user-designated chord progression. It
addresses the above issues through a generation-retrieval pipeline.
Specifically, our paradigm has two stages: (1) creation stage, where a huge
amount of music fragments generated by a neural melody language model are
indexed in a database through several key features (e.g., chords, tonality,
rhythm, and structural information); (2) re-creation stage, where melodies are
re-created by retrieving music fragments from the database according to the key
features from lyrics and concatenating best music fragments based on
composition guidelines and melody language model scores. ROC has several
advantages: (1) It only needs unpaired melody data to train melody language
model, instead of paired lyric-melody data in previous models. (2) It achieves
good lyric-melody feature alignment in lyric-to-melody generation. Tested by
English and Chinese lyrics, ROC outperforms previous neural based
lyric-to-melody generation models on both objective and subjective metrics.

本研究介绍了一种新的歌词旋律生成范例，即 Re-creation of Creations (ROC)，该范例通过将大量由神经旋律语言模型生成的音乐片段分阶段储存在数据库中，再根据作曲指南和旋律语言模型的重要性评分，检索音乐片段以从歌词和和弦中重新创作旋律，实现好的歌词旋律特征对齐。