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ANOMALOUS NOISE EVENTS CONSIDERATIONS FOR THE COMPUTATION OF ROAD TRAFFIC NOISE LEVELS: THE DYNAMAP'S MILAN CASE STUDY

机译:道路交通噪声水平计算的异常噪声事件考虑:DAMAMAP的米兰案例研究

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Dynamic noise maps are computed to represent the noise levels generated by road traffic in real time to determine the population exposure to environmental noise. To this aim, a network of acoustic sensors can be deployed in representative street locations to capture and process raw acoustic data before estimating the equivalent noise levels (L_(eq)) of the maps. In the framework of the DYNAMAP project, we conducted a study of the acoustic levels of several streets to determine the most suitable places for the sensors to be deployed in the case study of Milan city. The conclusions of that analysis were that the streets under test could be described by two different time-dependent behaviors, instead of the functional classification of roads. For a proper evaluation of the equivalent value L_(eq), several acoustic events existent in the street measurements have to be removed, since they are not generated by road traffic noise. These events, denoted as anomalous noise events, can come from sirens, horns, noisy human activities, etc. To that effect, the measured acoustic signal in the street has to be studied to classify whether the sample belongs to regular road traffic noise, or it is an anomalous noise event, assuming that the typology of these events is very diverse. In order to allow the deployment of the monitoring system, this evaluation should be conducted previously, and if possible, in the acoustic sensor sites resulting from the previous analysis. In this paper, we evaluate the different typologies of anomalous noise events observed in the city of Milan and we describe them statistically according to the identified street clusters. Moreover, we study the potential impact they may cause in the L_(eq) evaluation if not discarded.
机译:动态噪声图计算为即时实时代表道路交通产生的噪声水平,以确定人口暴露于环境噪声。为此目的,可以在代表性的街道位置部署声学传感器网络,以捕获和处理原始声学数据,然后估计地图的等效噪声电平(L_(eq))。在DAMAMAP项目的框架中,我们对几条街道的声学水平进行了研究,以确定在米兰市的案例研究中要部署的传感器最合适的地方。该分析的结论是测试所需的街道可以通过两个不同的时间依赖行为来描述,而不是道路的功能分类。对于对等效值L_(EQ)的适当评估,必须删除街道测量中存在的若干声学事件,因为它们不是由道路交通噪声产生的。这些事件,表示为异常噪声事件,可以来自警报器,角,嘈杂的人类活动等。对于这种影响,必须研究街道中的测量声信号,以分类样本是否属于常规道路交通噪声,或假设这些事件的类型是非常多样化的,这是一个异常的噪声事件。为了允许部署监测系统,此评估应该先进行,如果可能的话,在原始分析中产生的声学传感器站点中。在本文中,我们评估了在米兰市观察到的异常噪声事件的不同类型,我们根据所确定的街道群体统计描述它们。此外,如果没有丢弃,我们研究他们可能导致L_(EQ)评估的潜在影响。

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