In this work, we propose a symbolic music generation model with the song
structure graph analysis network. We construct a graph that uses information
such as note sequence and instrument as node features, while the correlation
between note sequences acts as the edge feature. We trained a Graph Neural
Network to obtain node representation in the graph, then we use node
representation as input of Unet to generate CONLON pianoroll image latent. The
outcomes of our experimental results show that the proposed model can generate
a comprehensive form of music. Our approach represents a promising and
innovative method for symbolic music generation and holds potential
applications in various fields in Music Information Retreival, including music
composition, music classification, and music inpainting systems.

提出了一种符号音乐生成模型，使用了歌曲结构图分析网络构建了一个图，利用音符序列和乐器等信息作为节点特征，音符序列之间的相关性作为边特征。通过训练图神经网络来获取图中节点的表示，然后将节点表示作为 Unet 的输入生成 CONLON 钢琴滚动图像潜变量。实验结果表明，该模型可以生成全面形式的音乐，为符号音乐生成提供了一种有前景且创新的方法，并在音乐信息检索的各个领域，包括音乐创作、音乐分类和音乐修复系统中具有潜在的应用价值。