CRCNS: Auditory Stream Segregation and the Cocktail Party Problem

描述(申请人提供)：听力以一种看似毫不费力的方式进行复杂的过程和转换，统称为听觉场景分析，通过这种分析，听觉系统将来自环境的声学信息整合到感知和认知体验中。这里提出的项目探索了听觉场景分析的一个基本感知成分，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注意到许多相互竞争的声学流中的一种-在文献中也被称为“鸡尾酒会问题”。虽然在人类、哺乳动物、鸟类和鱼类中完全是直觉的和无所不在的，但这种非凡的感知能力仍然笼罩在神秘之中。它很少在客观的心理声学测试中被量化，也很少在非人类物种中被研究，也很少在人类或动物的生理实验中被探索。因此，开发听觉流分离的计算模型的少数尝试仍然是高度推测的，缺乏感知和生理数据来支持他们的公式。这反过来又大大阻碍了工程系统中这种能力的发展，例如在自动语音识别或传感器网络中目标声音的检测和跟踪方面。 这项拟议的研究试图开发一种听觉场景分析的计算模型，该模型解释了听觉流分离的知觉和神经元发现。这项工作的智力价值是为设计新的流媒体心理声学和生理学实验提供了一个严格的框架，并为开发有效的算法实现来解决工程应用中的“鸡尾酒会问题”提供了一个严格的框架。拟议的研究项目利用了神经生物学家、心理声学家和工程师在整合心理声学、生理学和计算技术方面的专业知识。这一努力的更广泛影响是提供了通用和易处理的听觉流分离模型，从而显著促进了工程系统中此类功能的集成，例如在自动语音识别或传感器网络中目标声音的检测和跟踪中。该项目还将为新假说的设计和生成提供严格的基础，以便更好地理解积极倾听的神经基础

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