Computational Music Analysis
Computational Music Analysis
复制标题
计算音乐分析
DOI：
10.1007/978-3-319-25931-4_7
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Abdallah S
中科院分区：
文献类型：
--
作者：
Abdallah S
计算语言学的最新发展提供了通过在音乐语料库上引入概率模型（以语法的形式）来分析音乐结构的方法。它们可以从音乐语言的概率模型中生成惯用的句子，从而为它们所建模的音乐结构提供解释。本章基于计算语言学方法，回顾了使用语法进行音乐分析的历史和当前工作。我们概述了概率语法的理论，并使用 PRISM 说明了它们在 Prolog 中的实现。总结了我们从两种符号音乐语料库中的音高序列学习简单语法的概率的实验。结果支持了我们的观点，即概率语法是计算音乐分析的一个有前途的框架，但也表明需要进一步的工作来确定其相对于马尔可夫模型的优越性。
Recent developments in computational linguistics offer ways to approach the analysis of musical structure by inducing probabilistic models (in the form of grammars) over a corpus of music. These can produce idiomatic sentences from a probabilistic model of the musical language and thus offer explanations of the musical structures they model. This chapter surveys historical and current work in musical analysis using grammars, based on computational linguistic approaches. We outline the theory of probabilistic grammars and illustrate their implementation in Prolog using PRISM. Our experiments on learning the probabilities for simple grammars from pitch sequences in two kinds of symbolic musical corpora are summarized. The results support our claim that probabilistic grammars are a promising framework for computational music analysis, but also indicate that further work is required to establish their superiority over Markov models.