Most work on musical score models (a.k.a. musical language models) for music transcription has focused on describing the local sequential dependence of notes in musical scores and failed to capture their global repetitive structure, which can be a useful guide for transcribing music. Focusing on rhythm, we formulate several classes of Bayesian Markov models of musical scores that describe repetitions indirectly using the sparse transition probabilities of notes or note patterns. This enables us to construct piece-specific models for unseen scores with an unfixed repetitive structure and to derive tractable inference algorithms. Moreover, to describe approximate repetitions, we explicitly incorporate a process for modifying the repeated notes/note patterns. We apply these models as prior musical score models for rhythm transcription, where piece-specific score models are inferred from performed MIDI data by Bayesian learning, in contrast to the conventional supervised construction of score models. Evaluations using the vocal melodies of popular music showed that the Bayesian models improved the transcription accuracy for most of the tested model types, indicating the universal efficacy of the proposed approach. Moreover, we found an effective data representation for modelling rhythms that maximizes the transcription accuracy and computational efficiency.

大多数关于音乐转录的乐谱模型（又名音乐语言模型）的工作都集中在描述乐谱中音符的局部顺序依赖性，而未能捕捉到它们的全局重复结构，这可能是转录音乐的有用指南。专注于节奏，我们制定了几类乐谱贝叶斯马尔可夫模型，这些模型使用稀疏转换概率间接描述重复 音符或音符图案。这使我们能够为具有不固定重复结构的看不见的分数构建特定于片段的模型，并推导出易于处理的推理算法。此外，为了描述近似重复，我们明确地合并了一个修改重复音符/音符模式的过程。我们将这些模型用作节奏转录的先验乐谱模型，其中特定乐曲模型是通过贝叶斯学习从执行的 MIDI 数据中推断出来的，这与乐谱模型的传统监督构建形成对比。使用流行音乐的声乐旋律进行的评估表明，贝叶斯模型提高了大多数测试模型类型的转录准确性，表明所提出方法的普遍有效性。此外，我们发现了一种用于建模节奏的有效数据表示，可以最大限度地提高转录准确性和计算效率。