CRCNS: The role of sound statistics for discrimination and coding of sounds

CRCNS：声音统计在声音辨别和编码中的作用

• 批准号: 9090040
• 负责人: MONTY A ESCABI
• 金额: $ 29.35万
• 依托单位: UNIVERSITY OF CONNECTICUT STORRS
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9090040
• 关键词: Accounting; Acoustics; Address; Algorithms; Animals; Area; Auditory; Auditory area; Auditory system; Behavior; Biomedical Engineering; Brain; Cataloging; Catalogs; Categories; Classification; Cluster Analysis; Code; Collaborations; Complement; Computer software; Data; Databases; Development; Devices; Discrimination; Electrical Engineering; Electrodes; Engineering; Engineering Psychology; Enrollment; Environment; Fostering; Future; Hearing; Human; Individual; Inferior Colliculus; Internships; Knowledge; Laboratories; Lead; Machine Learning; Measures; Methods; Minority; Modeling; Music; Neuraxis; Neurons; Neurosciences; Noise; Oryctolagus cuniculus; Pattern; Physiology; Play; Population; Postdoctoral Fellow; Process; Prosthesis; Psychology; Reading; Research; Research Personnel; Resources; Role; Scientist; Signal Detection Analysis; Site; Speech; Statistical Distributions; Structure; System; Technology; Testing; Texture; Time; Training; Variant; Woman; Work; awake; base; computational neuroscience; doctoral student; graduate student; hearing impairment; man; neurophysiology; novel; novel strategies; online repository; programs; relating to nervous system; response; signal processing; sound; speech recognition; statistics; theories; trait; undergraduate student; web site

 DESCRIPTION (provided by applicant): Humans and other animals can discriminate and recognize sounds despite substantial acoustic variability in real-world sounds. This ability depends partly on the auditory system's ability to detect and utilize high-order statistical regularities that are present in the acoustic environment. Despite numerous advances in signal processing, assistive listening devices and speech recognition technologies lack biologically realistic strategies to dynamically deal with such acoustic variability. Thus, a comprehensive theory for how the central nervous system encodes and utilizes statistical structure in sounds is essential to develop processing strategies for sound recognition, coding and compression, and to assist individuals with hearing loss. This proposal presents a novel approach towards addressing the question of how the auditory system deals with and exploits statistical regularities for identification and discrimination of sounds in two critical mammalian auditory structures (inferior colliculus, IC; auditory cortex, AC) Aim 1 is to develop a catalogue of natural and man-made sounds and their associated high-order statistics. Cluster analysis and machine learning will be applied to the sound ensembles to identify salient statistical features that can be used to identify and categorize sounds from a computational perspective. Using information theoretic and correlation based methods, Aim 2 tests the hypothesis that statistical sound regularities are encoded in neural response statistics, including firing rate and spike-timing statistics of IC and AC neurons. Aim 3 will determine neurometric response functions and addresses the hypothesis that high-order statistical regularities in sounds can be discriminated based on temporal pattern and firing rate statistics of single neurons in IC and AC. Aim 4 will employ multi-site recording electrode arrays to tests the hypothesis that neural populations in IC and AC use high-order statistics for sound discrimination and that statistical regularities are encoded by regionally distributed differences n the strength and timing of neural responses or neuron-to-neuron correlations. The study will provide the groundwork for developing a general theory for how the brain encodes and discriminates sounds based on high-order statistical features. A catalogue of neural responses from single cells, neural ensembles, and high-level statistical features that differentiate real world sounds will be developed and deployed as an on-line resource. The role high-order statics play for sound recognition and discrimination will be identified both from a computational and neural coding perspective, including identifying transformations across neural structures, spatial and temporal scales. The project will foster collaborations between psychology, electrical engineering, and biomedical engineering departments at the UConn. Graduate, undergraduate and a post-doctoral student, including women and minorities, will participate in the research and will receive interdisciplinary training in areas of neurophysiology, computation neuroscience, and engineering. Drs. Read and Escabi regularly host summer interns in their labs and expect that 1-2 undergraduate students will be hosted per year. Graduate students will be enrolled in biomedical, electrical engineering, and psychology programs. Project findings will be integrated in graduate computational neuroscience and biomedical engineering coursework. The findings could lead to a host of new sound recognition technologies that make use of high-order statistical regularities to recognize and differentiate amongst sounds. Understanding how high-order statistics are represented in the brain could guide the development of optimal algorithms for detecting a target sound (e.g., speech) in variable/noisy conditions. Such sound recognition systems are also applicable in industrial applications: for instance, identifying fault machine systems from machine generated sounds. Knowledge of the statistical distributions in real world sounds and music will be useful for sound compression (e.g., mpeg coding) and to develop efficient sound processing algorithms. Finally, the findings can be incorporated in auditory prosthetics that mimic normal hearing physiology and make use of high-order sound statistics to remove background noise or enhance intelligibility.

 描述（由适用提供）：人类和其他动物可以区分和识别现实世界中的声音可变性。该能力部分取决于听觉系统检测和利用声学环境中存在的高阶统计规律性的能力。尽管在信号处理方面取得了许多进步，但辅助聆听设备和语音识别技术缺乏生物学现实的策略，无法动态处理这种声学变异性。这是关于中枢神经系统如何编码和使用声音中的统计结构的综合理论对于制定声音识别，编码和压缩的处理策略以及帮助听力损失的个人至关重要。 该提案提出了一种新的方法，以解决听觉系统如何处理和利用统计规律性的问题，以识别和歧视两个关键的哺乳动物听觉结构（下肌，IC； ACortory Cortex，ac）AIM 1是开发自然和人为声音及其相关的高级统计数据的目的。聚类分析和机器学习将应用于声音集合，以识别可用于从计算角度识别和类别声音的显着统计特征。使用信息理论和基于相关的方法，AIM 2检验统计声音规律性在神经反应统计中编码的假设， 包括IC和AC神经元的点火率和尖峰定时统计。 AIM 3将确定神经学响应功能，并解决以下假设：可以根据临时模式和发射速率统计数据来区分声音中的高阶统计规律性 IC和AC中的单神经元。 AIM 4将采用多站点记录电极阵列来检验以下假设：IC和AC中的神经量量使用高阶统计量来进行声音歧视，并且统计规律性是由区域分布的差异编码的，而神经回旋或神经元与神经性神经元相关的强度和时机。 这项研究将为开发一般理论提供基础，该理论如何根据高阶统计特征来编码和区分声音。来自单个细胞，神经元合奏和高级统计特征的神经元反应的目录将开发并部署为在线资源。高阶静态在声音识别和歧视方面发挥作用，将从计算和神经元编码的角度识别，包括识别神经结构，空间和临时尺度的转换。 该项目将促进UCONN的心理学，电气工程和生物医学工程部门之间的合作。毕业生，本科生和博士后学生，包括妇女和少数民族，将参加研究，并将获得跨学科 在神经生理学，计算神经科学和工程学领域进行培训。博士。阅读和Escabi定期将夏季旅馆主持在他们的实验室中，并预计每年将举办1-2名本科生。研究生将入学生物医学，电气工程和心理学计划。项目发现将集成到研究生计算神经科学和生物医学工程课程中。 这些发现可能会导致许多新的声音识别技术，这些技术利用高阶统计规律性来识别和区分声音。了解大脑中如何表示高阶统计数据可以指导在可变/嘈杂条件下检测目标声音（例如语音）的最佳算法的发展。这种声音识别系统也适用于工业应用：例如，从机器生成的声音中识别故障机系统。了解现实世界中的统计分布和音乐中的统计分布将对声音压缩（例如MPEG编码）有用，并开发有效的声音处理算法。最后，这些发现可以纳入模仿正常听力生理学的听觉假肢中，并利用高级声音统计来消除背景噪声或增强智能。