Chung King
Amplification and Communication Research Laboratory, Department of Allied Health and Communication Disorders, Northern Illinois University, DeKalb, IL, United States.
J Commun Disord. 2023 Sep-Oct;105:106351. doi: 10.1016/j.jcomdis.2023.106351. Epub 2023 Jul 13.
Audiometric calibration, which includes the calibration of different audiometer transducers and the measurements of ambient noise levels, is historically carried out using Class 1 sound level meters. As technologies advance, many mobile applications (apps) have been developed to measure sound levels. These apps can provide alternative methods for audiometric calibration in places where sound level meters are not available, such as field testing environments, low-to-mid-income countries, and humanitarian settings. These apps, however, cannot be used for audiometric calibration without first evaluating their performance, which depends on multiple factors including the external components (if any), the operating system and the hardware of the electronic devices. The evaluation of the apps is actually the evaluation of the app and associated factors (i.e., the app systems). This paper discusses methods to assess several key functions of apps implemented in either Android or iOS operation system for audiometric calibration: 1) checking the measurement accuracy at all testing frequencies, 2) deriving and using correction factors, 3) determining the self-noise levels, and 4) evaluating the linear/measurement range. As audiometric calibration usually uses octave or 1/3 octave bands to measure sound pressure levels of tones and narrowband noises with relatively steady temporal characteristics, the accuracy of an app can be evaluated by comparing the levels measured by the app and a Class 1 sound level meter at each frequency. The level difference between the app and the Class 1 sound level meter at each frequency can then be used to calculate correction factors that can be added to subsequent levels measured by the app to improve its accuracy. In addition, methods to determine the self-noise level and the linearity range of apps are discussed. Sample measurement scenarios and alternative methods are provided to illustrate the evaluation process to determine whether an app is suitable for measuring ambient noise levels and for calibrating different audiometric transducers.
听力计校准，包括不同听力计换能器的校准和环境噪声水平的测量，传统上是使用1级声级计进行的。随着技术的进步，许多移动应用程序（应用）已被开发用于测量声级。在声级计无法使用的地方，如现场测试环境、中低收入国家和人道主义环境中，这些应用可以为听力计校准提供替代方法。然而，在未首先评估其性能之前，这些应用不能用于听力计校准，其性能取决于多个因素，包括外部组件（如有）、操作系统和电子设备的硬件。对应用的评估实际上是对应用及其相关因素（即应用系统）的评估。本文讨论了评估在安卓或iOS操作系统中实现的用于听力计校准的应用的几个关键功能的方法：1）检查所有测试频率下的测量精度，2）推导和使用校正因子，3）确定自噪声水平，4）评估线性/测量范围。由于听力计校准通常使用倍频程或1/3倍频程频段来测量具有相对稳定时间特性的纯音和声压级以及窄带噪声，因此可以通过比较应用和1级声级计在每个频率下测量的声级来评估应用的准确性。然后，可以使用应用和1级声级计在每个频率下的声级差来计算校正因子，该因子可以添加到应用随后测量的声级中以提高其准确性。此外，还讨论了确定应用自噪声水平和线性范围的方法。提供了示例测量场景和替代方法来说明评估过程，以确定应用是否适合测量环境噪声水平和校准不同的听力计换能器。
