Auditory Processing of Complex Sounds

复杂声音的听觉处理

• 批准号: 8975186
• 负责人: Laurel H. Carney
• 金额: $ 43.61万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8975186
• 关键词: Accounting; Acoustic Nerve; Acoustics; Address; Aging; Algorithm Design; Algorithms; Auditory; Auditory system; Behavior; Behavioral; Behavioral Model; Birds; Brain Stem; Cochlear Implants; Code; Collaborations; Communication; Communication impairment; Complex; Computer Simulation; Cues; Data; Detection; Development; Discrimination; Feedback; Fiber; Frequencies; Future; Goals; Grant; Health; Hearing; Hearing Aids; Human; Inferior Colliculus; Labyrinth; Language; Lead; Link; Masks; Melopsittacus; Midbrain structure; Modeling; Neurons; Noise; Non-linear Models; Oryctolagus cuniculus; Pattern; Perception; Periodicity; Peripheral; Physiological; Population; Presbycusis; Process; Property; Public Health; Role; Self-Help Devices; Shapes; Signal Detection Analysis; Signal Transduction; Speech; Speech Sound; Stimulus; System; Techniques; Testing; Variant; Voice; Work; awake; base; behavioral study; experience; hearing impairment; improved; neural model; neuromechanism; novel; novel strategies; rate of change; relating to nervous system; response; signal processing; sound; tool

DESCRIPTION (provided by applicant): From a public health perspective, understanding the processing of speech sounds is critical in effecting significant improvement in the lives of people with communication disorders. The neural code for speech over the range of sound and noise levels experienced daily is elusive due to strong nonlinearities of the inner ear and central auditory neurons. In collaboration with a phonetician, we are studying neural responses to acoustic parameters that are crucial to differentiating speech sounds. This proposal focuses on the neural coding of vowels in quiet and in noise. The rationale for focusing on vowels is their fundamental role in carrying information, especially in discourse, and their centrality in all know speech systems. We have developed a novel, testable hypothesis for the robust representation in the midbrain of two salient features of vowels: fundamental frequency (F0), or voice pitch, and formant frequencies, the spectral peaks that differentiate vowels. This hypothesis takes into account the facts that i) in addition to having a best frequency (BF), most midbrain neurons are tuned for periodicities in the range of voice pitch, and ii) the strength of the periodicities in te response of the periphery changes systematically depending upon the relation between BF and formant frequency. In particular, the rate fluctuations of auditory-nerve (AN) responses that are synchronized to the F0 of a vowel are weak for fibers tuned near formant frequencies and strong for fibers tuned between formants. This variation in the amplitude of low-frequency rate fluctuations across the AN is propagated to the midbrain, where neurons sensitive to modulation frequency have large rate changes depending on the relation between BF and vowel formant frequencies. The profile of rates across midbrain neurons encodes the formant frequencies of vowels and is robust across a wide range of sound levels and in the presence of noise. This code is appropriately vulnerable to changes in peripheral tuning, decreases in the strength of peripheral nonlinearities such as synchrony capture, and to changes in central inhibitory processing associated with aging. Our vowel-coding hypothesis will be tested by quantitatively relating behavioral thresholds for detection and discrimination of formants to physiological responses at the level of the midbrain. We will further develop our models for signal processing in the auditory midbrain to include a nonlinear feature of neural processing, mode-locking, that is observed in the midbrain. We hypothesize that mode-locking contributes to the representation of strongly periodic sounds, such as voiced speech, by boosting the response of neurons with band-pass modulation tuning to strongly modulated sounds. This work will lead to the development of improved signal-processing algorithms to assist the growing number of people who are afflicted with hearing loss. Because the representation proposed by our vowel-coding hypothesis is fundamentally different from classical models for neural representations of speech sounds, the signal-processing strategies to restore it will differ fundamentally from existing strategies.

描述（由申请人提供）：从公共卫生的角度来看，了解语音的处理对于影响人们的生活显着改善至关重要 沟通障碍。由于内耳和中央听觉神经元的强烈非线性，每天都有声音和噪音水平的语音范围内的语音守则难以捉摸。与语音家合作，我们正在研究对隔离语音至关重要的声学参数的神经反应。该提案着重于安静和噪音中元音的神经编码。关注元音的理由是它们在携带信息中的基本作用，尤其是在话语中，以及它们在所有已知的语音系统中的中心地位。我们已经为中脑中的稳健表示形式开发了一个新颖的可检验假设，该中脑具有元音的两个显着特征：基本频率（F0）或语音音调和共振峰频率，这是区分元音的光谱峰。该假设考虑了以下事实：除了具有最佳频率（BF）之外，大多数中脑神经元还针对语音音调范围的周期性进行了调整，ii）周期性的周期性在周期性的响应中的强度根据BF和恒定频率之间的关系而系统地变化。特别是，与元音F0同步的听觉神经（AN）响应的速率波动对于接近强义频率的纤维而言较弱，并且对于在共振剂之间调节的纤维而言，纤维强。跨AN的低频速率波动振幅的这种变化传播到中脑，其中对调制频率敏感的神经元取决于BF和元音共振剂频率之间的关系较大。跨脑神经元的速率曲线编码元音的共振峰频率，并且在各种声音级别和存在噪声的情况下都具有稳健性。该代码非常容易受到周围调整的变化的影响，诸如同步捕获等周围非线性的强度以及与衰老相关的中央抑制处理的变化。我们的元音编码假设将通过定量的行为阈值来检验，以检测和歧视中间脑水平的生理反应。我们将进一步开发在听觉中脑中信号处理的模型，以包括在中脑中观察到的神经加工，模式锁定的非线性特征。我们假设模式锁定有助于通过增强带有带 - 调制调制的神经元对强烈调制声音的神经元的响应来表现强烈的周期性声音，例如声音语音。这项工作将导致改进的信号处理算法的发展，以帮助越来越多的听力损失的人。因为我们的元音编码假设提出的表示与语音声音的神经表示的经典模型根本不同，所以恢复的信号处理策略将与现有策略有所不同。