Studies involving soundscape perception often exclude participants with hearing loss to prevent impaired perception from affecting experimental results. Participants are typically screened with pure tone audiometry, the "gold standard" for identifying and quantifying hearing loss at specific frequencies, and excluded if a study-dependent threshold is not met. However, procuring professional audiometric equipment for soundscape studies may be cost-ineffective, and manually performing audiometric tests is labour-intensive. Moreover, testing requirements for soundscape studies may not require sensitivities and specificities as high as that in a medical diagnosis setting. Hence, in this study, we investigate the effectiveness of the uHear app, an iOS application, as an affordable and automatic alternative to a conventional audiometer in screening participants for hearing loss for the purpose of soundscape studies or listening tests in general. Based on audiometric comparisons with the audiometer of 163 participants, the uHear app was found to have high precision (98.04%) when using the World Health Organization (WHO) grading scheme for assessing normal hearing. Precision is further improved (98.69%) when all frequencies assessed with the uHear app is considered in the grading, which lends further support to this cost-effective, automated alternative to screen for normal hearing.
翻译:涉及声学认知的研究往往将听力损失的参与者排除在外,以防止损害感知影响实验结果。参与者通常用纯音量测度来筛选,即确定和量化特定频率听力损失的“黄金标准”,如果没有达到依赖研究的临界值,则排除。然而,为声学研究采购专业声学测量设备可能不具有成本效益,人工进行声学测试是劳力密集型的。此外,声学研究的测试要求可能不需要像医学诊断环境那样高的敏感度和特殊性。因此,在本研究中,我们调查了以声学研究或一般听力测试为目的,在筛选参与者的听力损失时,将iOS应用作为一种可负担得起的和自动的常规听力计替代品,作为筛选参与者的听力损失的一种替代方法。根据对163名参与者的音学计进行的声学比较,发现,在使用世界卫生组织(世卫组织)评估正常听力的分级办法时,uhear应用软件具有很高的精确度(98.04 % ) 。因此,在考虑采用正常听力测时,精确性(98.69% ) 将进一步提高(98.69%),因为所有频率评估的频率都被视为具有自动化,从而可以进一步支持正常的屏幕。