# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 3 Dynamap Approach
### The Dynamap Network for Z9: Implementation, Location, and Operation
#### 3.3.1 The Sensors Implementation and ANED  
The basic equation for predicting the traffic noise level at a site \(s\) is given by Equation (10), where the group noise variations, \(\delta^{(\tau)}(t,g_{i})\), are a function of the single sensors acquired data, \(\delta^{(\tau)}(t,s_{j}(g_{i}))\), as discussed in Section 3.2. In the realm of Dynamap, we define the latter as follows:  
\[\delta^{(\tau)}(t,s_{j}(g_{i}))=L_{\rm eq}^{(\tau)}(t,s_{j}(g_{i}))_{\rm meas }-L_{\rm eq}^{(1h)}(t_{\rm ref},s_{j}(g_{i}))_{\rm calc}, \tag{14}\]  
where \(L_{\rm eq}^{(\tau)}(t,s_{j}(g_{i}))_{\rm meas}\) are the measured acoustic levels by the sensor at the location \(s_{j}(g_{i})\), and \(L_{\rm eq}^{(1h)}(t_{\rm ref},s_{j}(g_{i}))_{\rm calc}\) the calculated reference acoustic levels obtained at the rush hour \(t_{\rm ref}\in\) (8:00-9:00) a.m. (see Figure 5 for details). These variations, which are of the same form as in Equation (8), can be used then either in Equation (11), or in Equations (12) and (13), to obtain the group variations \(\delta^{(\tau)}(t,g_{i})\).  
Due to the complexity of the urban noise scenario, both in space and time, an automated update of the noise maps is required. This is essentially the whole idea of the Dynamap system. One of the issues of the automated system is the content of the detected noise level that can include, in addition to the main noise source, i.e., the road traffic, the contribution of additional noise sources present in the mapping area. If these additional sources of noise are not filtered out, the resulting maps would not constitute a faithful reflection of the acoustic impact of road infrastructures. For this reason, it is necessary to endow the Dynamap system with the ability to discern between road traffic noise and other types of acoustic events (e.g., aircrafts, industries, works on the road, people talking, etc.), to exclude the latter from the noise level computation. To this end, an anomalous noise event detection (ANED) algorithm was developed [71; 76; 84; 85]. This algorithm operates on the audio stream captured by the acoustic sensors and identifies the presence of acoustic events unrelated to road traffic, activating an alert signal to exclude the corresponding audio passages from the computation of noise levels.  
The design of the ANED algorithm follows a \"detection-by-classification\" approach, consisting of the binary classification of sequential audio segments as either \"road traffic noise\" or \"anomalous noise event\". The algorithm is able to discern three main signal categories: road traffic noise, background city noise, and anomalous events. This latter class is further divided into 18 subtypes of events, such as people talking, music in car or in the street, or noise caused by tramways or trains, etc. The ANED algorithm is trained, validated, and tested using a data set containing samples of both road traffic and anomalous noise events taken from real environmental contexts [71; 72; 73; 74; 75; 76; 85].