Increase in population density in large cities has increased the environmental noise present in these environments, causing negative effects on human health. There are different sources of environmental noise; however, noise from road traffic is the most prevalent in cities. Therefore, it is necessary to have tools that allow noise characterization to establish strategies that permit obtaining levels that do not affect the quality of life of people. This research discusses the implementation of a system that allows the acquisition of data to characterize the noise generated by road traffic. First, the methodology for obtaining acoustic indicators with an electret measurement microphone is described, so that it adjusts to the data collection needs for road traffic noise analyses. Then, an approach for the classification and counting of automatic vehicular traffic through deep learning is presented. Results showed that there were differences of 0.2 dBA in terms of RMSE between a type 1 sound level meter and the measurement microphone used. With reference to vehicle classification and counting for four categories, the approximate error is between 3.3% and -15.5%.

大城市人口密度的增加增加了这些环境中存在的环境噪声，对人类健康造成了负面影响。环境噪声的来源多种多样。但是，道路交通产生的噪音在城市中最为普遍。因此，有必要使用允许噪声表征的工具来建立能够获得不影响人们生活质量的水平的策略。这项研究讨论了一个系统的实现，该系统允许获取数据以表征道路交通产生的噪声。首先，描述了用驻极体测量麦克风获得声学指标的方法，以便适应道路交通噪声分析的数据收集需求。然后，提出了一种通过深度学习对自动车辆流量进行分类和计数的方法。结果表明，类型1声级计和所使用的测量麦克风之间在RMSE方面存在0.2 dBA的差异。关于车辆分类和四个类别的计数，近似误差在3.3％和-15.5％之间。