论文标题
旋律条件的歌词一代与seqgans
Melody-Conditioned Lyrics Generation with SeqGANs
论文作者
论文摘要
多年来，自动歌词的生成一直受到音乐和AI社区的关注。由于数据驱动的模型中计算能力和演变的增加，基于规则的早期方法具有〜---〜主要是用深度学习的系统代替。但是，许多现有的方法要么很大程度上依赖音乐和歌词写作的先验知识，要么通过很大程度上抛弃旋律信息及其与文本的关系来过度简化任务。我们提出了一个基于序列生成对抗网络（SEQGAN）的端到端旋律条件的歌词生成系统，该系统生成了一系列歌词，并以相应的旋律为输入。此外，我们使用附加的输入条件研究了生成器的性能：要生成的歌词的主题或总体主题。我们表明，输入条件对评估指标没有负面影响，同时使网络能够产生更有意义的结果。
Automatic lyrics generation has received attention from both music and AI communities for years. Early rule-based approaches have~---due to increases in computational power and evolution in data-driven models---~mostly been replaced with deep-learning-based systems. Many existing approaches, however, either rely heavily on prior knowledge in music and lyrics writing or oversimplify the task by largely discarding melodic information and its relationship with the text. We propose an end-to-end melody-conditioned lyrics generation system based on Sequence Generative Adversarial Networks (SeqGAN), which generates a line of lyrics given the corresponding melody as the input. Furthermore, we investigate the performance of the generator with an additional input condition: the theme or overarching topic of the lyrics to be generated. We show that the input conditions have no negative impact on the evaluation metrics while enabling the network to produce more meaningful results.
