Probabilistic Auditory Scene Analysis

听觉环境通常非常复杂。例如，鸡尾酒会包括许多来源；玻璃杯的叮当声；许多客人叽叽喳喳；背景音乐的声音。然而，我们的听觉系统可以理解这种上升；它可以计算出有多少声源，并确定每个声源对场景的单独贡献。值得注意的是，它可以使用来自单个麦克风的信息来做到这一点。听觉神经科学的一个主要目标是了解听觉系统是如何实现这一壮举的。一般来说，人们认为听觉场景分析有三个阶段。第一个阶段在生理学上是很容易理解的，那就是将传入的声音转换成时间-频率表示。这揭示了在某一特定时间的频段内的局部能量。在第二阶段，心理物理证据表明，原始分组原则被用于对来自共同来源的频谱时间能量的局部区域进行分组。通过使用简单的刺激——比如音调和噪音——一长串原始分组原则已经被阐明。例如，良好的延续性原则识别单一来源的平滑变化特征和单独来源的突变特征。在听觉场景分析的最后一个阶段，称为基于模式的分组，更高层次的知识，如音乐或演讲的结构，被用来将频谱时间能量组绑定到流中，以便每个源都有一个流。这个框架有许多悬而未决的问题。一个重要的开放性问题是听觉皮层在听觉场景分析中所起的作用，因为它还没有得到很好的证实。另一个问题涉及已建立的原始分组规则列表的通用性和完整性。尽管这些原理成功地描述了对简单声音的感知，但对于自然声音的描述是否成功和相关尚不清楚。本项目旨在通过建模工作、心理物理实验和神经记录实验来解决这些问题。新的观点是将原始分组原则看作是由听觉场景的潜在变量模型中的相互影响而产生的。潜在变量模型描述了一个听觉场景，比如在鸡尾酒会上遇到的场景，是如何由潜在的听觉资源组成的，比如玻璃杯的叮当声和客人的喋喋不休声。它还包括对这些来源的统计数据的描述，比如玻璃杯的叮当声往往是孤立的、高频率的事件，而喋喋不休的声音则更恒定、更低频率。这个想法是，大脑试图利用他们的统计数据的先验知识来推断这些潜在的来源。新的概率推理工具可以使这些直觉具体化。这种被称为概率场景分析的新视角有两个优势；一个是实践的，一个是理论的。实际的优点是声音的统计特征可以用来产生具有复杂但可控结构的刺激，用于实验。理论上的好处是，原始分组规则的列表，以及它们相互权衡的方式，现在都是从声音的统计中得出的；不再需要启发式实现。这使我们能够预测实验的结果。特别是，心理物理学实验旨在解决听觉分组如何在合成听觉纹理（如雨、风、水等）中运作，以及这是否与概率解释一致。此外，神经记录实验将研究听觉皮层在听觉场景分析中的作用，并假设它代表了声音的高水平统计，就像缓慢变化的调节成分一样。

Auditory environments are typically very complicated. For example, thecocktail party comprises many sources; the chinking of glasses; thechattering of the many guests; the sound of backgroundmusic. Nevertheless, our auditory system can make sense of such ascene; it can work out how many acoustic sources there are anddetermine the individual contributions to the scene fromeach. Remarkably, it can do this using the information from a singlemicrophone. A major goal of auditory neuroscience is to understandhow the auditory system achieves this feat.Broadly speaking, it is thought that there are three stages toauditory scene analysis. The first stage is well understoodphysiologically and that is to convert the incoming sound into atime-frequency representation. This reveals the local energy in afrequency band at a particular time. In the second stage,psychophysical evidence suggests that primitive grouping principlesare used to group local regions of spectral-temporal energy arisingfrom a common source. By using simple stimuli - like tones and noise -a long list of primitive grouping principles have been elucidated. Forexample, the principle of good continuation identifies smoothlyvarying features with a single source and abrupt changes as asignature of separate sources. In the final stage of auditory sceneanalysis, called schema-based grouping, higher level knowledge, likethe structure of music or speech, is used to bind the groups ofspectral-temporal energy into streams so that there is one stream foreach source.There are many outstanding questions with this framework. Oneimportant open question is the role that auditory cortex plays inauditory scene analysis as it is not well established. Anotherconcerns the generality and completeness of the established list ofprimitive grouping rules. For although the principles successfullycharacterise perception of simple sounds it is unclear how successfuland relevant the description is for natural sounds. This project aims to resolve these questions though modelling work,psychophysics experiments and neural recording experiments. The newidea is to view the primitive grouping principles as arising frominference in a latent variable model of auditory scenes. A latentvariable model is a description of how an auditory scene, like thatencountered at a coctail party, is composed of latent auditorysources, like the chinking glasses and chattering guests. It alsoincludes a description of the statistics of these sources, like thefact that the chinking glasses tend to be isolated, high frequencyevents whist the chattering rather more constant and lower infrequency. The idea is that the brain is trying to infer these latentsources using prior knowledge of their statistics. New tools ofprobabilistic inference can make these intuitions concrete.This new perspective, called probabilistic scene analysis, has twomain advantages; one practical and one theoretical. The practicaladvantage is that a statistical characterisation of sounds can be usedto produce stimuli with complicated, but controlled structure, for usein experiments. The theoretical benefit is that the list of primitivegrouping rules, and the manner in which they trade off, are nowderived from the statistics of sounds; Heuristic implementation is nolonger required. This enables us to predict the results of theexperiments. In particular, the psychophysics experiments are aimedat resolving both how auditory grouping operates in synthetic auditorytextures (e.g. rain, wind, water etc.) and whether this is consistentwith the probabilistic account. Furthermore, the neural recordingexperiments will investigate the role of auditory cortex in auditoryscene analysis, and the hypothesis that it is representing high levelstatistics of sounds like slowly varying modulatory components.