Auditory Processing of Complex Sounds

描述（由申请人提供）：健康的耳朵能够比任何人造算法或系统更好地检测和识别噪声环境中的信号。然而，轻度至中度的感觉神经性听力损失会给处于嘈杂环境中的听众带来很大的困难。我们正在结合行为，生理和计算建模方法来研究复杂声音的神经处理。近年来的努力集中在掩蔽，从所有三种方法的结果导致了在低频掩蔽检测的交叉频率重合检测模型的发展。我们将此模型称为相位相干模型-它利用了调谐到不同频率的AN纤维的相对放电时间。该模型提供了一个生理上现实的替代经典的功率谱（或“能量”）模型的掩蔽和解决与该模型相关的基本问题。我们建议测试的假设，建议的相位相干性模型在低频噪声中的音调掩蔽检测，并将我们的研究扩展到高频率和其他掩蔽范例。在这些研究中，我们将继续结合联合收割机在人类和兔子的行为实验，在清醒的兔子和麻醉沙鼠的生理记录，和计算建模。我们还将继续使用在过去几年中开发的建模工具，这些工具不仅可以捕获健康和受损的神经纤维和中枢听觉神经元的详细特性，而且还可以将基于群体模型预测的性能与行为性能的实际测量进行定量比较。这项工作的长期目标是提供新的信号处理策略，以帮助听力受损，基于独特的神经策略处理噪声中的信号。

DESCRIPTION (provided by applicant): The healthy ear is able to detect and identify signals in a noisy environment better than any manmade algorithm or system. However, mild-to-moderate sensorineural hearing loss causes significant difficulty for listeners in noisy environments. We are combining behavioral, physiological, and computational-modeling approaches to study neural processing of complex sounds. The efforts of recent years have focused on masking, and results from all three approaches have led to the development of a cross-frequency coincidence-detection model of masked detection at low frequencies. We refer to this model as the phase-opponency model - it takes advantage of the relative times of discharge of AN fibers tuned to different frequencies. This model provides a physiologically realistic alternative to the classical power-spectrum (or "energy") model for masking and resolves fundamental problems associated with that model. We propose to test hypotheses, suggested by the phase-opponency model regarding masked detection of tones in noise at low frequencies and to extend our studies to high frequencies and to other masking paradigms. Throughout these studies, we will continue to combine behavioral experiments in human and rabbit, physiological recordings in awake rabbit and anesthetized gerbil, and computational modeling. We will also continue to use modeling tools developed during the last few years that not only capture the detailed properties of healthy and impaired auditory-nerve fibers and central auditory neurons, but that also allow quantitative comparison of performance predicted on the basis of population models to actual measurements of behavioral performance. The long-term goal of this effort is to provide novel signal-processing strategies to aid the hearing impaired, based on the unique neural strategies for processing signals in noise.