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中文摘要
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项目摘要/摘要 也许听力受损或植入人工耳蜗的听众面临的最普遍的问题是 在有相互竞争的声源的情况下识别语音和其他声音的困难,如当 在餐馆里聊天。“声音分离”中的困难--当听到一个特别感兴趣的声音时 被嵌入到其他声音的混合中--通常会导致沮丧和社会孤立,而且不足以 通过目前的助听器和植入物来解决。健全的隔离困难也经常报告在 发育性听觉障碍。这项拟议研究的长期目标是揭示声音的基础 并提供有助于改进假肢装置和补救策略的见解, 以及用于处理声音的更有效的机器系统,例如用于自动语音识别。这个 目前,更有效的设备、技术和治疗方法的开发受到不完整的 了解听力正常的听者分离声音的因素。特别是,几乎没有 已知的声音分离带有复杂的自然主义声音,部分原因是在这方面的许多研究 区域已经使用简单的信号进行,相对于通常收听声音的人来说,这些信号是贫乏的 相遇。我们建议通过三组实验来丰富对声音分离的理解 使用新的声音合成方法来操纵自然语音和其他声音的属性,并测试它们的 人类听者行为实验在隔离中的作用。目标1操纵了经典提出的 由谐波频率关系提供的分组线索,并研究其背后的机制 效果。目标2调查声音和言语结构的先验知识对隔离的作用,并应 帮助解释为什么有些声音比其他声音更容易或更难区分。目标3调查了 注意跟踪从混合中分离的声音,并将探索促进跟踪的因素 或者导致它失败。这一结果将揭示健康人听觉分离声音的机制。 系统,并将提供对在多种情况下限制听觉理解的因素的洞察 声源,希望为信号增强、假肢设备和 行为矫正。
英文摘要
PROJECT SUMMARY / ABSTRACT Perhaps the most pervasive problem faced by listeners with hearing impairment or cochlear implants is the difficulty of recognizing speech and other sounds in the presence of competing sound sources, as when conversing at a restaurant. This difficulty in “sound segregation” – hearing a particular sound of interest when it is embedded in a mixture of other sounds – often leads to frustration and social isolation, and is not adequately addressed by current hearing aids and implants. Sound segregation difficulties are also commonly reported in developmental auditory disorders. The long-term goal of the proposed research is to reveal the basis of sound segregation and to provide insights that will facilitate improved prosthetic devices and remediation strategies, as well as more effective machine systems for processing sounds, e.g. for automatic speech recognition. The development of more effective devices, technologies, and therapies is currently limited by an incomplete understanding of the factors that underlie sound segregation by normal-hearing listeners. In particular, little is known about sound segregation with complex naturalistic sounds, in part because much of the research in this area has been conducted using simple signals that are impoverished relative to the sounds listeners normally encounter. We propose to enrich the understanding of sound segregation with three sets of experiments that use novel sound synthesis methods to manipulate properties of natural speech and other sounds and test their role in segregation with behavioral experiments in human listeners. Aim 1 manipulates the classically proposed grouping cue provided by harmonic frequency relations and investigates the mechanisms underlying their effect. Aim 2 investigates the role of prior knowledge of voice and speech structure on segregation, and should help to explain why some voices are easier or harder to segregate than others. Aim 3 investigates the role of attentive tracking in the segregation of sounds from mixtures, and will explore the factors that facilitate tracking or cause it to fail. The results will reveal the mechanisms underlying sound segregation by the healthy auditory system, and will provide insights into the factors that limit auditory comprehension in the presence of multiple sound sources, hopefully suggesting new strategies for signal enhancement, prosthetic devices, and behavioral remediation.
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Computational Cognitive Neuroscience of Human Auditory Cortex
Computational Cognitive Neuroscience of Human Auditory Cortex
Computational Cognitive Neuroscience of Human Auditory Cortex
Auditory Scene Analysis with Complex Sounds
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