论文标题
来自歌词的可解释旋律生成具有离散值的对抗训练
Interpretable Melody Generation from Lyrics with Discrete-Valued Adversarial Training
论文作者
论文摘要
在人工智能和音乐领域中，从歌词中产生旋律是一项有趣但又具有挑战性的任务。但是，保持输入歌词和产生的旋律之间的一致性的困难限制了以前作品的发电质量。在我们的提案中，我们演示了我们提出的可解释的歌词到旋律生成系统，该系统可以与用户互动以了解生成过程并重新创建所需的歌曲。为了提高与歌词相匹配的旋律生成的可靠性，相互信息被利用以增强歌词和生成的旋律之间的一致性。利用Gumbel-Softmax来解决通过生成对抗网络（GAN）生成离散音乐属性的非差异性问题。此外，发电机的预测概率输出用于推荐音乐属性。与我们的歌词到旋律生成系统进行互动，用户可以收听生成的AI歌曲，并通过从推荐的音乐属性中进行选择来重新创建新歌。
Generating melody from lyrics is an interesting yet challenging task in the area of artificial intelligence and music. However, the difficulty of keeping the consistency between input lyrics and generated melody limits the generation quality of previous works. In our proposal, we demonstrate our proposed interpretable lyrics-to-melody generation system which can interact with users to understand the generation process and recreate the desired songs. To improve the reliability of melody generation that matches lyrics, mutual information is exploited to strengthen the consistency between lyrics and generated melodies. Gumbel-Softmax is exploited to solve the non-differentiability problem of generating discrete music attributes by Generative Adversarial Networks (GANs). Moreover, the predicted probabilities output by the generator is utilized to recommend music attributes. Interacting with our lyrics-to-melody generation system, users can listen to the generated AI song as well as recreate a new song by selecting from recommended music attributes.
