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Music motif, as a conceptual building block of composition, is crucial formusic structure analysis and automatic composition. While human listeners canidentify motifs easily, existing computational models fall short inrepresenting motifs and their developments. The reason is that the nature ofmotifs is implicit, and the diversity of motif variations extends beyond simplerepetitions and modulations. In this study, we aim to learn the implicitrelationship between motifs and their variations via representation learning,using the Siamese network architecture and a pretraining and fine-tuningpipeline. A regularization-based method, VICReg, is adopted for pretraining,while contrastive learning is used for fine-tuning. Experimental results on aretrieval-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 intuitivecomprehension of the overall structure of a music piece. As far as we know,this work marks a noteworthy step forward in computational modeling of musicmotifs. We believe that this work lays the foundations for future applicationsof motifs in automatic music composition and music information retrieval.

Motif-Centric Representation Learning for Symbolic Music