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An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments

机译：现实生活中的城市和郊区环境中动态道路交通噪声映射的异常噪声事件检测器

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One of the main aspects affecting the quality of life of people living in urban and suburban areas is their continued exposure to high Road Traffic Noise (RTN) levels. Until now, noise measurements in cities have been performed by professionals, recording data in certain locations to build a noise map afterwards. However, the deployment of Wireless Acoustic Sensor Networks (WASN) has enabled automatic noise mapping in smart cities. In order to obtain a reliable picture of the RTN levels affecting citizens, Anomalous Noise Events (ANE) unrelated to road traffic should be removed from the noise map computation. To this aim, this paper introduces an Anomalous Noise Event Detector (ANED) designed to differentiate between RTN and ANE in real time within a predefined interval running on the distributed low-cost acoustic sensors of a WASN. The proposed ANED follows a two-class audio event detection and classification approach, instead of multi-class or one-class classification schemes, taking advantage of the collection of representative acoustic data in real-life environments. The experiments conducted within the DYNAMAP project, implemented on ARM-based acoustic sensors, show the feasibility of the proposal both in terms of computational cost and classification performance using standard Mel cepstral coefficients and Gaussian Mixture Models (GMM). The two-class GMM core classifier relatively improves the baseline universal GMM one-class classifier F1 measure by 18.7% and 31.8% for suburban and urban environments, respectively, within the 1-s integration interval. Nevertheless, according to the results, the classification performance of the current ANED implementation still has room for improvement.
机译：影响城市和郊区居民生活质量的主要方面之一是他们持续暴露于高水平的道路交通噪声（RTN）中。迄今为止，专业人员已经在城市进行了噪声测量，并在某些位置记录数据以随后建立噪声图。但是，无线声学传感器网络（WASN）的部署已启用了智能城市中的自动噪声映射。为了获得影响居民的RTN级别的可靠图像，应从噪声图计算中删除与道路交通无关的异常噪声事件（ANE）。为此，本文介绍了一种异常噪声事件检测器（ANED），用于在WASN的分布式低成本声学传感器上运行的预定义间隔内实时区分RTN和ANE。拟议的ANED遵循两类音频事件检测和分类方法，而不是多类或一类分类方案，它利用了现实环境中代表性声音数据的收集优势。在基于ARM的声传感器上实施的DYNAMAP项目中进行的实验表明，使用标准梅尔倒谱系数和高斯混合模型（GMM），该建议在计算成本和分类性能方面都是可行的。两类GMM核心分类器在1-s集成间隔内分别将郊区和城市环境的基准通用GMM一类分类器F1度量分别提高了18.7％和31.8％。但是，根据结果，当前ANED实施的分类性能仍有改进的空间。

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