Music motif, as a conceptual building block of composition, is crucial for
music structure analysis and automatic composition. While human listeners can
identify motifs easily, existing computational models fall short in
representing motifs and their developments. The reason is that the nature of
motifs is implicit, and the diversity of motif variations extends beyond simple
repetitions and modulations. In this study, we aim to learn the implicit
relationship between motifs and their variations via representation learning,
using the Siamese network architecture and a pretraining and fine-tuning
pipeline. A regularization-based method, VICReg, is adopted for pretraining,
while contrastive learning is used for fine-tuning. Experimental results on a
retrieval-based task show that these two methods complement each other,
yielding an improvement of 12.6% in the area under the precision-recall curve.
Lastly, we visualize the acquired motif representations, offering an intuitive
comprehension of the overall structure of a music piece. As far as we know,
this work marks a noteworthy step forward in computational modeling of music
motifs. We believe that this work lays the foundations for future applications
of motifs in automatic music composition and music information retrieval.

通过使用 Siamese 网络架构和预训练及微调流程，本研究旨在通过表示学习来学习旋律和其变化之间的内在关系。实验证明，这两种方法相互补充，在检索任务中使精确度 - 召回率曲线下的面积提高了 12.6%。最后，我们可视化所获得的旋律表示，以直观地理解音乐作品的整体结构。据我们所知，这项工作在计算建模音乐旋律方面迈出了值得关注的一步，为未来的自动音乐创作和音乐信息检索应用奠定了基础。