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（由申请人提供）：人类和其他动物可以区分和识别声音，尽管在现实世界的声音有很大的声学变化。这种能力部分取决于听觉系统检测和利用存在于声学环境中的高阶统计噪声的能力。尽管在信号处理方面取得了许多进展，但辅助听力设备和语音识别技术缺乏生物学上现实的策略来动态地处理这种声学变化。因此，中枢神经系统如何编码和利用声音中的统计结构的综合理论对于开发声音识别，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。 该建议提出了一种新的方法来解决这个问题的听觉系统如何处理和利用统计识别和歧视的声音在两个关键的哺乳动物听觉结构（下丘，IC，听觉皮层，AC）的目的1是开发一个目录的自然和人造的声音和他们相关的高阶统计。聚类分析和机器学习将应用于声音集合，以确定可用于从计算角度识别和分类声音的显著统计特征。使用信息论和基于相关性的方法，目标2测试了统计声音信号编码在神经反应统计中的假设， 包括IC和AC神经元的放电率和尖峰定时统计。目标3将确定神经测量反应功能，并提出假设，即高阶统计的声音可以根据时间模式和放电率统计来区分， IC和AC的单个神经元。目标4将采用多位点记录电极阵列来测试以下假设：IC和AC中的神经群体使用高阶统计来进行声音辨别，并且统计学特征由神经响应或神经元与神经元相关性的强度和时间的区域分布差异编码。 这项研究将为开发大脑如何根据高阶统计特征编码和区分声音的一般理论奠定基础。一个目录的神经反应，从单细胞，神经合奏，和高层次的统计特征，区分真实的世界的声音将被开发和部署作为一个在线资源。高阶静力学在声音识别和辨别中的作用将从计算和神经编码的角度来确定，包括识别神经结构，空间和时间尺度的转换。 该项目将促进康州大学心理学、电气工程和生物医学工程系之间的合作。研究生，本科生和博士后学生，包括妇女和少数民族，将参加研究，并将获得跨学科的 神经生理学、计算神经科学和工程学领域的培训。Read和Escabi博士定期在他们的实验室接待暑期实习生，并希望每年接待1-2名本科生。研究生将就读于生物医学，电气工程和心理学课程。项目研究结果将整合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一系列新的声音识别技术，这些技术利用高阶统计量来识别和区分声音。了解高阶统计在大脑中的表现方式可以指导用于检测目标声音的最佳算法的开发（例如，语音）在可变/噪声条件下。这种声音识别系统也可应用于工业应用中：例如，从机器产生的声音中识别故障机器系统。真实的世界声音和音乐中的统计分布的知识对于声音压缩（例如，MPEG编码）并开发有效的声音处理算法。最后，研究结果可以被纳入听觉假体，模仿正常的听力生理，并利用高阶声音统计，以消除背景噪声或提高可懂度。