HEARING HAZARD ASSOCIATED WITH INDUSTRIAL NOISE EXPOSURE

描述：(改编自《调查者摘要》)考虑到 工业和军事噪声环境是非高斯的，能量 通常用于评估的指标(即加权等效能量，LEQ) 噪声暴露对听力的不利影响仅是合适的指标。 对于高斯噪声，调查者建议开发和测试 另一种噪声分析方法的有效性 听力保护将更准确地预测听力测量和 暴露的形态后果。具体地说，调查员 将显示与统计指标相结合的能量指标 频域峰度和时间域峰度的联合峰间距直方图 将提供必要的(可能是充分的)基本信息 任何工业噪音环境，以评估其潜在的造成 听力损失。动物(龙猫)将暴露在非高斯环境中， 具有与高斯相同的能量和频谱的非平稳噪声 参考噪声(将使用具有频谱的两个参考噪声条件 和工业环境的典型水平参数)。噪声刺激 具有非常具体但多样化的统计属性的设计将是 使用最近设计的软件制作。开发了新的分析方法 在过去的三年里，在调查员的实验室里 基于小波变换和高阶累积量的逆滤波 应用于连续采样的噪声刺激以提取时间和 噪声刺激的峰值统计特性。对聆讯的影响， 通过纯音阈值、耳声发射和感觉细胞进行量化 损失将与噪声度量相关联，以建立有效性 这些指标中。考虑到解析式的算法 上述方法已经开发出来，可以集成到 商业噪声分析系统，这些成功的结果 实验可以为新的、更普遍的方法奠定基础， 以及更准确的噪声环境评估方法。 所提出的分析方法也可能具有一定的工程应用价值。 在识别噪声环境的特征时，可以减少或 在源头上发生了改变。重要的是要了解 噪音对听力的危害最大，因为工程程序可以 在机器的特定噪声产生部件上实施或 设计成听力保护装置。由结果组成的数据库 至少408名受试者通过两个对照或32个不同的 将构建复杂的噪声暴露条件。样本量大 而曝光参数的广泛变化是必要的，以确保 将被开发的相关性的统计能力。而当 本方案中的实验方法都是常规的，论证效果良好 建议的指标与以下听力损失之间的相关性 现实和多样化的暴露条件具有广泛的影响 工业安全标准和噪声测量系统。

DESCRIPTION: (Adapted from Investigator's Abstract) Considering that many industrial and military noise environments are non-Gaussian, and that energy metrics (i.e., a weighted equivalent energy, Leq) commonly used to assess the adverse effects of a noise exposure on hearing are suitable metrics only for Gaussian noise, the investigator proposes to develop and test the validity of an alternate approach to noise analysis for the purpose of hearing conservation which will more precisely predict the audiometric and morphological consequences of an exposure. Specifically, the investigator will show that an energy metric in combination with the statistical metrics of frequency- and time-domain kurtosis and the joint peak-interval histogram will provide necessary (and possibly sufficient) information on essentially any industrial noise environment to evaluate its potential for causing hearing loss. Animals (chinchillas) will be exposed to non-Gaussian, non-stationary noises having the same energy and spectra of a Gaussian reference noise (two reference noise conditions will be used having spectral and level parameters typical of an industrial environment). Noise stimuli designed with very specific but diverse statistical properties will be produced using recently-designed software. New analytical methods developed in the investigator's laboratories over the past three years involving the wavelet transform and higher-order cumulant-based inverse filtering will be applied to the continuously sampled noise stimuli to extract temporal and peak statistical properties of the noise stimulus. Effects on hearing, quantified by pure-tone thresholds, otoacoustic emissions, and sensory cell losses will be correlated with the noise metrics to establish the validity of these metrics. Considering that the algorithms for the analytical methods mentioned above have been developed and can be integrated into commercial noise analysis systems, the successful outcome of these experiments can lay the foundations for a new and more generalized approach, as well as a more accurate approach to the evaluation of noise environments. The suggested analytical methods may also have some engineering applications in identifying features of the noise environment that can be reduced or altered at their source. It is important to understand what features of a noise are most hazardous to hearing in order that engineering procedures can be implemented on specific noise-producing components of machinery or designed into hearing protective devices. A database consisting of results from at least 408 subjects run through one of two control or 32 different complex noise exposure conditions will be constructed. A large sample size and a wide variation of exposure parameters are necessary to insure statistical power for the correlations that will be developed. While the experimental methods in this proposal are routine, the demonstration of good correlations between the proposed metrics and hearing loss following realistic and diverse exposure conditions has widespread implications for industrial safety standards and noise measurement systems.