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信息动力学与音乐时间结构的感知

Information Dynamics and the Perception of Temporal Structure in Music
课程网址: http://videolectures.net/mbc07_abdallah_idt/  
主讲教师: Samer A. Abdallah
开课单位: 伦敦大学学院
开课时间: 2007-12-29
课程语种: 英语
中文简介:
人们经常观察到,听音乐在听众中产生期望的一个更显着的影响,以及制作音乐的艺术部分创造了不确定性,期望,实现和惊喜的动态相互作用。然而,直到Shannon的信息理论工作出版,这些工具才能量化其中的一些概念。从那以后,人们对信息理论与音乐和审美感知之间的关系一直存在着零星的兴趣。在本次演讲中,我们将研究在动态演化的概率模型的上下文中计算的少量\ emph {时变}信息度量(如熵和互信息)如何用于表征时间结构。刺激序列,从贝叶斯观察者的角度来看被视为随机过程。一个这样的衡量标准是一种新颖的\ emph {预测信息率},我们推测这可能为简单的随机性测量(例如熵率)和审美价值判断之间经常发现的“倒U”关系提供解释(Berlyne 1971) )。我们使用人工生成的序列和菲利普·格拉斯的两首极简主义音乐在马尔可夫链的背景下探索这些想法,表明即使是过于简单的模型(马尔可夫链),当根据信息动态原理进行解释时,也会产生结构分析。这在很大程度上与专业人类听众的意见一致。我们还将讨论如何将相同的原理应用于比完全观察到的马尔可夫链(特别是隐马尔可夫模型)更复杂的模型,通过使用在线变分贝叶斯方法来跟踪观察者关于未观测变量的(概率)信念。
课程简介: It has often been observed that one of the more salient effects of listening to music to create expectations within the listener, and that part of the art of making music to create a dynamic interplay of uncertainty, expectation, fulfilment and surprise. It was not until the publication of Shannon's work on information theory, however, that the tools became available to quantify some of these concepts. Since then, there has been sporadic interest in the relationship between information theory and music and aesthetic perception in general. \\ In this talk, we will examine how a small number of \emph{time-varying} information measures, such as entropies and mutual informations, computed in the context of a dynamically evolving probabilistic model, can be used to characterise the temporal structue of a stimulus sequence, considered as a random process from the point of view of a Bayesian observer. \\ One such measure is a novel \emph{predictive information rate} which we conjecture may provide an explanation for the `inverted-U' relationship often found between simple measures of randomness (\eg entropy rate) and judgements of aesthetic value (Berlyne 1971). We explore these ideas in the context of Markov chains using both artificially generated sequences and two pieces of minimalist music by Philip Glass, showing that even an overly simple model (the Markov chain), when interpreted according to information dynamic principles, produces a structural analysis which largely agrees with that of an expert human listener. \\ We will also discuss how the same principles can be applied to models more complex than the fully observed Markov chain (in particular, hidden Markov models), by using online variational Bayesian methods to track the observer's (probabilistic) beliefs about unobserved variables.
关 键 词: 信息理论; 音乐; 概率模型
课程来源: 视频讲座网
最后编审: 2020-10-22:chenxin
阅读次数: 58