  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.
