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Effects of noise on mental performance and annoyance considering task difficulty level and tone components of noise.
Journal of Environmental Health Science and Engineering ( IF 3.4 ) Pub Date : 2019-04-16 , DOI: 10.1007/s40201-019-00353-2
Mohammad Javad Jafari 1, 2 , Marzieh Sadeghian 1, 2 , Ali Khavanin 3 , Soheila Khodakarim 4 , Amir Salar Jafarpisheh 5
Affiliation  

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.

中文翻译:

考虑到任务的难度水平和噪声的语调成分,噪声对心理行为和烦恼的影响。

机械系统中的旋转部件会产生声音,这些声音的存在会影响乘员的质量和舒适度,从而导致烦人和心理行为下降。ISO 1996-2和ANSI S1.13标准已经描述了量化突出音调效果的度量标准,但是还需要更多有关噪音属性如何影响烦恼和性能的研究,尤其是在不同级别的任务难度下。本文研究了在不同任务难度级别下暴露于具有音调成分的背景噪声时,噪声指标,烦恼反应与心理表现之间的关系。在这个研究中,对60名参与者进行了主观感知烦恼和不同工作量的评估,同时在受控的测试室中暴露了18种具有三种不同声调的噪声信号(三个频率声调和两个背景噪声级别),同时又进行了三种不同级别的n背任务。通过记录反应时间,正确的速率和未命中次数来测量性能参数。结果表明,在较高的任务难度水平下,未命中次数和反应时间的增加趋势,但对于正确率的下降趋势。研究结果表明,在不同的任务难度水平下,除了烦恼和响度外,主观反应存在显着差异。参与者对更高的背景噪音水平感到更恼火,降低音调频率并提高音调水平,尤其是在任务难度增加的情况下。响度指标与其他噪音指标高度相关。基于使用神经网络模型的最紧密相关的噪声度量,提出了三种预测感知烦恼的模型。这三个模型中的每一个都有不同的输入参数和不同的网络结构。这三个神经网络模型的准确性和MSE均表明它适用于预测感知到的烦恼。结果表明,声调噪声对烦恼和心理行为的影响,尤其是在不同工作难度下。结果还表明,神经网络模型具有较高的准确性和效率,并可用于预测噪声烦恼。在某些方面(例如较低的输入参数,使其更加人性化。最佳的神经网络模型同时包括响度指标和音调指标。似乎在所提出的神经网络模型中,组合指标的重要性最低,并且是不必要的。
更新日期:2019-04-16
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