CRCNS: Auditory Stream Segregation and the Cocktail Party Problem

描述（由申请人提供）：听觉以一种看似毫不费力的方式参与复杂的过程和转换，统称为听觉场景分析，通过听觉场景分析，听觉系统将来自环境的声学信息整合为感知和认知体验。这里提出的项目探讨了一个基本的感知组成部分的听觉场景分析称为听觉流分离。这种现象表现在人类和动物即使在极端嘈杂和混响的环境中也能够注意到许多竞争声流中的一个-在文献中也称为“鸡尾酒会问题”。虽然人类、哺乳动物、鸟类和鱼类都具有完全的直觉和无所不在的感知能力，但这种非凡的感知能力仍然笼罩在神秘之中。它很少在客观心理声学测试中量化或在非人类物种中进行研究，也很少在人类或动物的生理实验中进行探索。因此，在开发听觉流分离的计算模型方面的一些尝试仍然是高度推测性的，并且缺乏感知和生理数据来支持它们的公式。这反过来又大大阻碍了工程系统中这种能力的发展，例如自动语音识别或传感器网络中目标声音的检测和跟踪。 拟议的研究旨在开发一个计算模型的听觉场景分析，占听觉流分离的感知和神经元的调查结果。这项工作的智力价值是提供了一个严格的框架，设计新的心理声学和生理实验的流，并开发有效的算法实现，以解决工程应用中的“鸡尾酒会问题”。拟议的研究项目借鉴了神经生物学家，心理声学家和工程师在整合心理声学，生理和计算技术的专业知识。这一努力的更广泛的影响是在提供多功能和易于处理的模型的听觉流隔离，因此显着促进工程系统，如在自动语音识别或检测和跟踪传感器网络中的目标声音的这种能力的集成。 这个项目还将为设计和产生新的假设提供严格的基础，以便更好地理解主动倾听的神经基础

DESCRIPTION (provided by applicant): Hearing engages, in a seemingly effortless way, complex processes and transformations collectively known as auditory scene analysis, through which the auditory system consolidates acoustic information from the environment into perceptual and cognitive experiences. The project proposed here explores a fundamental perceptual component of auditory scene analysis called auditory stream segregation. This phenomenon manifests itself in the ability of humans and animals to attend to one of many competing acoustic streams even in extreme noisy and reverberant environments - also known in the literature as the "Cocktail Party Problem". While completely intuitive and omnipresent in humans, mammals, birds, and fish, this remarkable perceptual ability remains shrouded in mystery. It has been rarely quantified in objective psychoacoustical tests or investigated in non-human species, and seldom explored in physiological experiments in humans or animals. Consequently, the few attempts at developing computational models of auditory stream segregation remain highly speculative, and lack the perceptual and physiological data to support their formulations. This in turn has considerably hindered the development of such capabilities in engineering systems such as in automatic speech recognition or the detection and tracking of target sounds in sensor networks. The proposed research seeks to develop a computational model of auditory scene analysis that accounts for perceptual and neuronal-findings of auditory stream segregation. The intellectual merit of this work is providing a rigorous framework for the design of new psychoacoustic and physiological experiments of streaming, and for developing effective algorithmic implementations to tackle the "cocktail party problem" in engineering applications. The proposed research project draws upon the expertise of neurobiologists, psychoacousticians, and engineers in integrating psychoacoustic, physiological and computational techniques. The broader impact of this effort is in providing versatile and tractable models of auditory stream segregation, and hence significantly facilitating the integration of such capabilities in engineering systems, such as in automatic speech recognition or the detection and tracking of target sounds in sensor networks. This project will also provide a rigorous foundation for the design and generation of new hypotheses in order to better understand the neural basis of active listening