CRCNS: The Role of Statistical Structure for Natural Sound Recognition in Noise

在存在竞争性背景噪声的情况下倾听和识别声音的能力是至关重要的 健康的听觉系统的功能。听力正常的人甚至可以很容易地进行对话 噪音水平相对较高，并处于复杂的听觉环境中，如繁忙的餐厅。然而， 对于听力受损的人来说，即使是中等水平的背景噪音也会对声音产生不利影响 承认。因此，理解噪声中识别的神经机制是非常重要的。 在临床上有相关性。 这个项目提供了一种新的方法来研究健康的听觉系统如何利用统计学 在存在相互竞争的自然背景声音的情况下，在真实世界的声音识别过程中的声音提示。 使用人类参与者聆听自然声音混合和人工修改的变体，目标1 探讨几种声音纹理统计如何影响真实世界噪声中的语音感知。目标2 测试假设，相同的统计声音提示调节听觉中脑神经活动和 这些统计数据对前景的神经表征产生了有利或不利的影响 自然噪音中的声音。神经解码器随后将评估神经活动如何对 各种自然噪声下的识别，具有明显的统计特性。最后，在Aim 3中，一个模型捕获了 将使用外周和中枢听觉系统转换来机械地预测神经 在不利的自然掩蔽条件下，活动、神经解码性能和人类识别。 我们假设，通过捕捉中央听觉系统的基本变化，我们将 能够预测基于神经的识别和人类感知趋势。 这项研究将为发展听觉系统如何利用的一般理论奠定基础。 高阶统计结构，用于背景环境噪声中自然、逼真的声音识别。 这一结果将通过量化高阶线索对听觉掩蔽的影响来扩展先前关于听觉掩蔽的工作 感知，将提供统计线索如何驱动非经典神经反应的详细描述， 并将定义可以同时解释生理和行为的模型。调查结果、模型和 将开发的最佳质量指标具有与健康相关的影响，可能会有所改善 人类交流的结果，可能应用于听力诊断和生物学发育 用于听觉假体的灵感噪声抑制策略。

The ability to listen and identify sounds in the presence of competing background noise is a critical function of the healthy auditory system. Humans with normal hearing can easily carry a conversation even with relatively high levels of noise and in complex auditory environments, such as a busy restaurant. Yet, for individuals with hearing loss even moderate levels of background noise can adversely impact sound recognition. Understanding the neural mechanisms that underlie recognition in noise is thus of high clinically relevance. This project provides a novel approach to study how the healthy auditory system utilizes statistical sound cues during real-world sound recognition in the presence of competing natural background sounds. Using human participants listening to natural sound mixtures and synthetically modified variants, Aim 1 explores how several sound texture statistics influence the perception of speech in real-world noise. Aim 2 tests the hypothesis that the same statistical sound cues modulate neural activity in auditory midbrain and that these statistics influence, beneficially or detrimentally, the neural representation of a foreground sounds in natural noises. Neural decoders will then assess how the neural activity contributes towards recognition under various natural noises with distinct statistics. Finally, in Aim 3, a model that captures peripheral and central auditory system transformations will be used to mechanistically predict neural activity, neural decoding performance, and human recognition, under adverse natural masking conditions. We hypothesize that, by capturing the fundamental transformations of the central auditory system, we will be able to predict neural-based recognition and human perceptual trends. The study will lay a foundation for developing a general theory of how the auditory system utilizes high-order statistical structure for natural, realistic sound recognition in background environmental noise. The results will extend previous work on auditory masking by quantify the influence of high-order cues on perception, will provide detailed descriptions of how statistical cues drive non-classical neural responses, and will define models that can account for both physiology and behavior. The findings, models, and optimal quality metrics that will be developed have health related implications that can potentially improve human communication outcomes, with likely applications for hearing diagnosis and developing biologically inspired noise suppression strategies for auditory prosthetics.