Lead sheets have become commonplace in generative music research, being used as an initial compressed representation for downstream tasks like multitrack music generation and automatic arrangement. Despite this, researchers have often fallen back on deterministic reduction methods (such as the skyline algorithm) to generate lead sheets when seeking paired lead sheets and full scores, with little attention being paid toward the quality of the lead sheets themselves and how they accurately reflect their orchestrated counterparts. To address these issues, we propose the problem of conditional lead sheet generation (i.e. generating a lead sheet given its full score version), and show that this task can be formulated as an unsupervised music compression task, where the lead sheet represents a compressed latent version of the score. We introduce a novel model, called Lead-AE, that models the lead sheets as a discrete subselection of the original sequence, using a differentiable top-k operator to allow for controllable local sparsity constraints. Across both automatic proxy tasks and direct human evaluations, we find that our method improves upon the established deterministic baseline and produces coherent reductions of large multitrack scores.

翻译：主旋律谱在生成式音乐研究中已变得普遍，常作为下游任务（如多轨音乐生成与自动编曲）的初始压缩表示。尽管如此，研究者在寻求配对的主旋律谱与完整总谱时，往往依赖确定性压缩方法（如天际线算法），鲜少关注主旋律谱自身的质量及其与配器版本间的准确映射关系。为解决上述问题，我们提出条件式主旋律谱生成任务（即：给定完整总谱版本生成其主旋律谱），并证明该任务可形式化为无监督音乐压缩任务——其中主旋律谱代表总谱的压缩潜在表示。我们引入一种名为Lead-AE的新型模型，通过可微分top-k算子将主旋律谱建模为原始序列的离散子集选择，从而实现可控的局部稀疏性约束。通过自动代理任务与直接人工评估，我们发现该方法优于已建立的确定性基线，并能生成大型多轨总谱的连贯压缩表示。