Jafari Mohammad Javad, Sadeghian Marzieh, Khavanin Ali, Khodakarim Soheila, Jafarpisheh Amir Salar
1Environmental and Occupational Hazards Control Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Environ Health Sci Eng. 2019 Apr 16;17(1):353-365. doi: 10.1007/s40201-019-00353-2. eCollection 2019 Jun.
Rotating components in mechanical systems produce tonal noises and the presence of these tones effect the quality and comfort of occupants leading to annoyance and a decrease in mental performance. The ISO 1996-2 and ANSI S1.13 standards have described metrics to quantify the effects of prominent tones, but more research on how noise attributes effect annoyance and performance, especially in different levels of task difficulty are necessary. This paper investigates relations between noise metrics, annoyance responses and mental performance under different task difficulty levels while exposed to background noise with tonal components. In this study, sixty participants were evaluated on subjective perceived annoyance and varying workloads while exposed to 18 noise signals with three different prominence tones at three frequency tones and two background noise levels while doing three different levels of n-back tasks in a controlled test chamber. Performance parameters were measured by recording the reaction time, the correct rate, and the number of misses. The results indicate an increasing trend for number of misses and reaction times at higher task difficulty levels, but a decrease for correct rate. The study results showed a significant difference for subjective responses except for annoyance and loudness under different levels of task difficulty. The participants were more annoyed with higher background noise levels, lower tone frequencies and increasing tone levels especially under increasing task difficulty. Loudness metrics highly correlate with other noise metrics. Three models for the prediction of perceived annoyance are presented based on the most strongly correlated noise metrics using neural network models. Each of the three models had different input parameters and different network structures. The accuracy and MSE of all three neural network models show it to be appropriate for predicting perceived annoyance. The results show the effect of tonal noise on annoyance and mental performance especially in different levels of task difficulty. The results also suggest that neural network models have high accuracy and efficiency, and can be used to predict noise annoyance. Model 1 is preferred in certain aspects, such as lower input parameters, making it more user-friendly. The best neural network model included both loudness metrics and tonality metrics. It seems that combined metrics have the least importance and are unnecessary in the proposed neural network model.
机械系统中的旋转部件会产生音调噪声，这些音调的存在会影响居住者的质量和舒适度，导致烦恼并降低心理表现。ISO 1996 - 2和ANSI S1.13标准已经描述了量化突出音调影响的指标，但关于噪声属性如何影响烦恼和表现，尤其是在不同任务难度水平下的研究还需要更多。本文研究了在不同任务难度水平下，当暴露于带有音调成分的背景噪声时，噪声指标、烦恼反应和心理表现之间的关系。在这项研究中，60名参与者在一个受控测试室中进行三种不同水平的n - 回溯任务时，在暴露于18种具有三种不同突出音调、三个频率音调以及两种背景噪声水平的噪声信号的情况下，对主观感知的烦恼和不同工作量进行了评估。通过记录反应时间、正确率和失误次数来测量表现参数。结果表明，在较高任务难度水平下，失误次数和反应时间呈上升趋势，但正确率下降。研究结果表明，除了烦恼和响度外，不同任务难度水平下的主观反应存在显著差异。参与者对较高的背景噪声水平、较低的音调频率以及增加的音调水平更烦恼，尤其是在任务难度增加的情况下。响度指标与其他噪声指标高度相关。基于使用神经网络模型的最强烈相关噪声指标，提出了三种预测感知烦恼的模型。这三种模型中的每一种都有不同的输入参数和不同的网络结构。所有三种神经网络模型的准确性和均方误差表明其适用于预测感知烦恼。结果显示了音调噪声对烦恼和心理表现的影响，尤其是在不同任务难度水平下。结果还表明，神经网络模型具有较高的准确性和效率，可用于预测噪声烦恼。模型1在某些方面更受青睐，例如输入参数较低，使其更便于用户使用。最佳神经网络模型包括响度指标和音调指标。在所提出的神经网络模型中，组合指标似乎重要性最低且不必要。
