Socoró Joan Claudi, Alías Francesc, Alsina-Pagès Rosa Ma
GTM-Grup de recerca en Tecnologies Mèdia, La Salle, Universitat Ramon Llull, Quatre Camins, 30, 08022 Barcelona, Spain.
Sensors (Basel). 2017 Oct 12;17(10):2323. doi: 10.3390/s17102323.
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.
影响城市和郊区居民生活质量的主要因素之一是他们持续暴露在高水平的道路交通噪声（RTN）中。到目前为止，城市中的噪声测量工作一直由专业人员进行，他们在特定地点记录数据，以便之后绘制噪声地图。然而，无线声学传感器网络（WASN）的部署使得智能城市能够实现自动噪声地图绘制。为了获得影响市民的RTN水平的可靠情况，与道路交通无关的异常噪声事件（ANE）应从噪声地图计算中去除。为此，本文介绍了一种异常噪声事件检测器（ANED），旨在在WASN的分布式低成本声学传感器上运行的预定义时间间隔内实时区分RTN和ANE。所提出的ANED采用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，利用现实生活环境中代表性声学数据的收集。在基于ARM的声学传感器上实施的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），该方案在计算成本和分类性能方面都是可行的。在1秒积分时间间隔内，两类GMM核心分类器相对于基线通用GMM一类分类器的F1测量值，在郊区和城市环境中分别相对提高了18.7%和31.8%。然而，根据结果，当前ANED实现的分类性能仍有改进空间。
