Improved Metrical Alignment of Midi Performance Based on a Repetition-aware Online-adapted Grammar
Improved Metrical Alignment of Midi Performance Based on a Repetition-aware Online-adapted Grammar
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DOI：
10.1109/icassp.2019.8683808
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Andrew Mcleod;Eita Nakamura;Kazuyoshi Yoshii
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文献类型：
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作者：
Andrew Mcleod;Eita Nakamura;Kazuyoshi Yoshii
本文对现有的基于语法的格律结构检测和对齐方法进行了改进，该方法涉及将重复的树结构与音符输入流对齐。之前的方法可以获得最先进的结果，但在缺乏训练数据时表现不佳。按要求注释的数据并不广泛可用，这使得该方法的缺点很明显。我们提出了一种新的在线学习技术，利用动态学习的片段特定语法来提高语法在看不见的节奏模式上的表现。特定于片段的语法可以在不需要任何训练数据的情况下测量底层对齐的音乐格式良好性。相反，它依赖于音乐的重复和自相似性，使模型能够识别重复的节奏模式，即使在训练数据中从未见过类似的模式。使用它，我们看到在一个只包含巴赫作品的语料库上的表现有所改善，以及第二个包含各种作曲家作品的语料库，这表明在线学习的语法帮助模型概括了看不见的节奏和风格。
This paper presents an improvement on an existing grammar-based method for metrical structure detection and alignment, a task which involves aligning a repeated tree structure with an input stream of musical notes. The previous method achieves state-of-the-art results, but performs poorly when it lacks training data. Data annotated as it requires is not widely available, making this drawback of the method significant. We present a novel online learning technique to improve the grammar’s performance on unseen rhythmic patterns using a dynamically learned piece-specific grammar. The piece-specific grammar can measure the musical well-formedness of the underlying alignment without requiring any training data. It instead relies on musical repetition and self-similarity, enabling the model to recognize repeated rhythmic patterns, even when a similar pattern was never seen in the training data. Using it, we see improved performance on a corpus containing only Bach compositions, as well as a second corpus containing works from a variety of composers, indicating that the online-learned grammar helps the model generalize to unseen rhythms and styles.