Abstract Recently, many deep learning models have been proposed to generate symbolic melodies. However, generating pop music melodies with well organized structures remains to be challenging. In this paper, we present a melody structure-based model called PopMNet to generate structured pop music melodies. The melody structure is defined by pairwise relations, specifically, repetition and sequence, between all bars in a melody. PopMNet consists of a Convolutional Neural Network (CNN)-based Structure Generation Net (SGN) and a Recurrent Neural Network (RNN)-based Melody Generation Net (MGN). The former generates melody structures and the latter generates melodies conditioned on the structures and chord progressions. The proposed model is compared with four existing models AttentionRNN, LookbackRNN, MidiNet and Music Transformer. The results indicate that the melodies generated by our model contain much clearer structures compared to those generated by other models, as confirmed by human behavior experiments.

中文翻译：

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PopMNet：使用神经网络生成结构化的流行音乐旋律

摘要 最近，已经提出了许多深度学习模型来生成符号旋律。然而，生成具有良好组织结构的流行音乐旋律仍然具有挑战性。在本文中，我们提出了一个基于旋律结构的模型，称为 PopMNet 来生成结构化的流行音乐旋律。旋律结构由旋律中所有小节之间的成对关系定义，特别是重复和序列。PopMNet 由基于卷积神经网络 (CNN) 的结构生成网络 (SGN) 和基于循环神经网络 (RNN) 的旋律生成网络 (MGN) 组成。前者产生旋律结构，后者产生以结构和和弦进行为条件的旋律。将提出的模型与现有的四个模型 AttentionRNN、LookbackRNN、MidiNet 和 Music Transformer 进行比较。