基于第一性原理的计算音乐分析
Computational music analysis from first principles
AI总结：
本文用耦合隐马尔可夫模型自动标注 371 首巴赫众赞歌，在几乎不使用人工输入的情况下，和弦与调性识别准确率达到 85% 以上，为西方和声理论的客观性争论提供证据。
摘要：
我们使用耦合隐马尔可夫模型自动标注 Riemenschneider 版本中的 371 首巴赫众赞歌，该语料库包含约 100,000 个音符和 20,000 个和弦。我们给出三种独立分析，它们以对音乐句法做出越来越强的假设为代价，逐步获得更高的准确率。尽管我们的方法几乎不使用人工输入，但与专家人工分析相比，我们仍能以 85% 或更高的准确率识别和弦和调性，从而得到足够准确的标注，可用于一系列音乐理论目的，同时不受主观人类判断的影响。我们的工作关系到关于标准西方和声理论所假设的结构是否具有客观实在性的长期争论，也关系到关于西方和声句法本质的具体问题。
英文摘要：
We use coupled hidden Markov models to automatically annotate the 371 Bach chorales in the Riemenschneider edition, a corpus containing approximately 100,000 notes and 20,000 chords. We give three separate analyses that achieve progressively greater accuracy at the cost of making increasingly strong assumptions about musical syntax. Although our method makes almost no use of human input, we are able to identify both chords and keys with an accuracy of 85% or greater when compared to an expert human analysis, resulting in annotations accurate enough to be used for a range of music-theoretical purposes, while also being free of subjective human judgments. Our work bears on longstanding debates about the objective reality of the structures postulated by standard Western harmonic theory, as well as on specific questions about the nature of Western harmonic syntax.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
