A Robust Parser-Interpreter for Jazz Chord Sequences
A Robust Parser-Interpreter for Jazz Chord Sequences
复制标题
爵士和弦序列的强大解析器解释器
DOI：
10.1080/09298215.2014.910532
复制
发表时间：
2014
影响因子：
1.1
通讯作者：
Mark Steedman
中科院分区：
文献类型：
--
作者：
Mark Granroth;Mark Steedman
摘要 类似于语言中韵律和句法的层次结构可以在西方音调音乐的节奏和和声进行中找到。分析这种音乐结构类似于自然语言解析：它需要从高度模糊的元素（就音乐而言，音符）的非结构化序列中得出潜在的解释。这里的任务不仅仅是确定该序列是否符合语法，而是确定它在大量分析中属于哪一个。此类分析是熟悉音乐习语的听众（无论是否受过音乐训练）进行的认知处理的一部分。我们的重点是分析和谐级数所产生的期望和解决方案的结构。在之前的工作的基础上，我们定义了音调和声进行理论，其作用类似于语言中的语义。我们的解析器使用爵士乐和弦序列的形式语法（一种广泛用于自然语言处理（NLP）的类型），以表演者使用的和弦序列的形式将音乐映射到和弦之间的结构化关系的表示上。它使用 NLP 中广泛覆盖解析的统计建模技术，使实际解析在语法中存在相当大的歧义时变得可行。通过对一小部分带有和声分析注释的爵士乐和弦序列进行机器学习，我们发现使用简单统计解析模型的基于语法的音乐解释比基线 HMM 更准确。实验表明，源自自然语言处理的统计技术可以有效地应用于谐波结构的分析。
Abstract Hierarchical structure similar to that associated with prosody and syntax in language can be identified in the rhythmic and harmonic progressions that underlie Western tonal music. Analysing such musical structure resembles natural language parsing: it requires the derivation of an underlying interpretation from an unstructured sequence of highly ambiguous elements—in the case of music, the notes. The task here is not merely to decide whether the sequence is grammatical, but rather to decide which among a large number of analyses it has. An analysis of this sort is a part of the cognitive processing performed by listeners familiar with a musical idiom, whether musically trained or not. Our focus is on the analysis of the structure of expectations and resolutions created by harmonic progressions. Building on previous work, we define a theory of tonal harmonic progression, which plays a role analogous to semantics in language. Our parser uses a formal grammar of jazz chord sequences, of a kind widely used for natural language processing (NLP), to map music, in the form of chord sequences used by performers, onto a representation of the structured relationships between chords. It uses statistical modelling techniques used for wide-coverage parsing in NLP to make practical parsing feasible in the face of considerable ambiguity in the grammar. Using machine learning over a small corpus of jazz chord sequences annotated with harmonic analyses, we show that grammar-based musical interpretation using simple statistical parsing models is more accurate than a baseline HMM. The experiment demonstrates that statistical techniques adapted from NLP can be profitably applied to the analysis of harmonic structure.
DOI：
10.1093/acprof:oso/9780199553426.001.0001
发表时间：
2011
期刊：
--
影响因子：
--
作者：
P. Rebuschat;M. Rohrmeier;J. Hawkins;I. Cross
通讯作者：
P. Rebuschat;M. Rohrmeier;J. Hawkins;I. Cross