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

CRCNS：统计结构在噪声中自然声音识别中的作用

• 批准号: 10396135
• 负责人: MONTY A ESCABI
• 金额: $ 35.11万
• 依托单位: UNIVERSITY OF CONNECTICUT STORRS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10396135
• 关键词: Affect; Auditory; Auditory system; Behavior; Behavioral; Carrying Capacities; Cochlea; Code; Complex; Computer Models; Cues; Data; Discrimination; Electrodes; Frequencies; Human; Individual; Inferior Colliculus; Influentials; Masks; Modeling; Neurons; Noise; Oryctolagus cuniculus; Output; Perception; Population; Role; Source; Speech; Speech Discrimination; Stimulus; Structure; Study models; Testing; Texture; Variant; Vision; base; behavioral study; improved; neural correlate; neurophysiology; receptive field; relating to nervous system; response; sound; speech in noise; speech recognition; statistics; trend; vocalization

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.

在竞争背景噪音的存在下倾听和识别声音的能力是关键 健康听觉系统的功能。正常听力的人甚至可以轻松进行对话 噪音相对较高，在复杂的听觉环境中，例如繁忙的餐厅。然而， 对于听力损失的人，即使是中等水平的背景噪声也会对声音产生不利影响 认出。因此，了解噪声中识别基础的神经机制很高 临床上的相关性。 该项目提供了一种研究健康听觉系统如何利用统计的新方法 在存在竞争性的自然背景声音的情况下，在现实世界中识别的声音提示。 使用人类参与者聆听自然声音混合物和合成修改的变体，AIM 1 探索几种声音纹理统计信息如何影响现实世界噪声中语音的感知。目标2 检验以下假设：相同的统计声音提示调节听觉中脑的神经活动和 这些统计数据在有益或有害的是前景的神经表示 在自然的声音中听起来。然后，神经解码器将评估神经活动如何有助于 在各种自然噪声下具有不同统计数据的识别。最后，在AIM 3中，一个捕获的模型 外围和中央听觉系统转换将用于机械预测神经 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，通过捕获中央听觉系统的基本转换，我们将 能够预测基于神经的识别和人类感知趋势。 这项研究将奠定基础，以开发一般理论，说明听觉系统如何利用 背景环境噪声中自然，逼真的声音识别的高阶统计结构。 结果将通过量化高级线索对听觉掩蔽的先前工作来扩展对听觉掩盖的研究 感知，将提供详细的描述统计线索如何驱动非经典神经反应的方式， 并将定义可以考虑生理和行为的模型。发现，模型和 将开发的最佳质量指标具有与健康相关的含义，可以潜在地改善 人类交流结果，可能适用于听力诊断和生物学发展 启发了听觉假肢的噪声抑制策略。