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Learning symbolic music representations, especially disentangledrepresentations with probabilistic interpretations, has been shown to benefitboth music understanding and generation. However, most models are onlyapplicable to short-term music, while learning long-term music representationsremains a challenging task. We have seen several studies attempting to learnhierarchical representations directly in an end-to-end manner, but these modelshave not been able to achieve the desired results and the training process isnot stable. In this paper, we propose a novel approach to learn long-termsymbolic music representations through contextual constraints. First, we usecontrastive learning to pre-train a long-term representation by constrainingits difference from the short-term representation (extracted by anoff-the-shelf model). Then, we fine-tune the long-term representation by ahierarchical prediction model such that a good long-term representation (e.g.,an 8-bar representation) can reconstruct the corresponding short-term ones(e.g., the 2-bar representations within the 8-bar range). Experiments show thatour method stabilizes the training and the fine-tuning steps. In addition, thedesigned contextual constraints benefit both reconstruction anddisentanglement, significantly outperforming the baselines.

Learning long-term music representations via hierarchical contextual constraints