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

在竞争性背景噪声存在下倾听和识别声音的能力是至关重要的。 健康的听觉系统。听力正常的人可以很容易地进行对话， 具有相对高的噪声水平和复杂的听觉环境，例如忙碌的餐馆。然而， 对于听力损失的人来说，即使是中等水平的背景噪音也会对声音产生不利影响。 识别.因此，了解噪声中识别的神经机制具有重要意义。 临床相关性。 该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 在存在竞争的自然背景声音的情况下，在真实世界的声音识别期间的声音提示。 使用人类参与者听自然声音混合和合成修改的变体，目标1 探讨了几种声音纹理统计如何影响现实世界噪声中的语音感知。目的2 测试假设，相同的统计声音线索调节听觉中脑的神经活动， 这些统计数据有利地或有害地影响前景的神经表征， 在自然的噪音中。然后，神经解码器将评估神经活动如何有助于 在各种自然噪声下的识别具有明显的统计特性。最后，在目标3中，一个模型， 外周和中枢听觉系统转换将被用于机械地预测神经系统的变化。 活动，神经解码性能和人类识别，在不利的自然掩蔽条件下。 我们假设，通过捕捉中央听觉系统的根本转变，我们将 能够预测基于神经的识别和人类感知趋势。 这项研究将为发展一个关于听觉系统如何利用 高阶统计结构，用于在背景环境噪声中自然、逼真的声音识别。 这些结果将通过量化高阶线索对听觉掩蔽的影响来扩展以前的工作。 知觉，将提供如何统计线索驱动非经典神经反应的详细描述， 并将定义可以解释生理和行为的模型。研究结果、模型和 将开发的最佳质量指标具有与健康相关的含义， 人类交流成果，可能应用于听力诊断和生物学发展 启发噪声抑制策略的听觉假肢。

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.