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Modeling the Rhythm from Lyrics for Melody Generation of Pop Song | | Daiyu Zhang
; Ju-Chiang Wang
; Katerina Kosta
; Jordan B. L. Smith
; Shicen Zhou
; | | Date: |
3 Jan 2023 | | Abstract: | Creating a pop song melody according to pre-written lyrics is a typical
practice for composers. A computational model of how lyrics are set as melodies
is important for automatic composition systems, but an end-to-end
lyric-to-melody model would require enormous amounts of paired training data.
To mitigate the data constraints, we adopt a two-stage approach, dividing the
task into lyric-to-rhythm and rhythm-to-melody modules. However, the
lyric-to-rhythm task is still challenging due to its multimodality. In this
paper, we propose a novel lyric-to-rhythm framework that includes
part-of-speech tags to achieve better text setting, and a Transformer
architecture designed to model long-term syllable-to-note associations. For the
rhythm-to-melody task, we adapt a proven chord-conditioned melody Transformer,
which has achieved state-of-the-art results. Experiments for Chinese
lyric-to-melody generation show that the proposed framework is able to model
key characteristics of rhythm and pitch distributions in the dataset, and in a
subjective evaluation, the melodies generated by our system were rated as
similar to or better than those of a state-of-the-art alternative. | | Source: | arXiv, 2301.01361 | | Services: | Forum | Review | PDF | Favorites | |
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