论文标题
从中文歌词通过共同信息最大化的各种旋律产生
Diverse Melody Generation from Chinese Lyrics via Mutual Information Maximization
论文作者
论文摘要
在本文中，我们建议将相互信息最大化的方法调整到中国歌词的任务中，从而提高了旋律的生成，以提高发电质量和多样性。我们采用计划的抽样和强制解码技术来改善歌词和旋律之间的一致性。借助我们称之为多样的旋律产生（DMG）的方法，序列到序列模型学会了根据输入样式ID产生多种旋律，同时保持音调和改善对齐方式。主观测试的实验结果表明，与基线方法相比，DMG可以产生更多的令人愉悦和相干的曲调。
In this paper, we propose to adapt the method of mutual information maximization into the task of Chinese lyrics conditioned melody generation to improve the generation quality and diversity. We employ scheduled sampling and force decoding techniques to improve the alignment between lyrics and melodies. With our method, which we called Diverse Melody Generation (DMG), a sequence-to-sequence model learns to generate diverse melodies heavily depending on the input style ids, while keeping the tonality and improving the alignment. The experimental results of subjective tests show that DMG can generate more pleasing and coherent tunes than baseline methods.
