## 2 Related Work  
In recent years, progress has been made in the deployment of different acoustic sensor networks designed for noise monitoring, especially in urban areas, where traffic noise is one of the main sources of annoyance described in the END [4]. The observation of the noise measured in each of the stations can lead to the classification of their locations, according to the measured level of noise, but also according to other urban parameters, such as the type of street, or the intensity of traffic measured or estimated by the municipalities.  
Several studies can be found in the literature on the cluster analysis of urban acoustic environments. In [17], the authors introduced a clustering approach based on the k-means algorithm, considering yearly acoustic indexes, such as \(L_{day}\), \(L_{evening}\) and \(L_{night}\), as well as the standard deviation of \(L_{den}\). The sensor network under test was the Barcelona Noise Monitoring Network [22], which also measured noise levels with the final goal of the identification of several acoustic environments across the city. The results showed that the obtained clusters have a geographical meaning, as they correspond to locations close to high traffic roads, residential areas or leisure areas, respectively. Other studies, as [18], start with an approximation of road classification based on the road design and the traffic speed data. Subsequently, the equivalent noise levels of all the roads is included in the clustering. The final goal of the study conducted in Foshan (China) was to predict the equivalent noise level from vehicles' speed and road design information. In [23], the authors propose a method to draw a spatio-temporal distribution of the noise levels in the city with two variables, the traffic density and the traffic speed, as well as spatio-temporal characteristics derived from the geography of the deployed network. The goal of the study was to evaluate the noise distributions corresponding to several periods by efficient algorithms of prediction with an acceptable accuracy.  
The Milan pilot of the DYNAMAP project was widely analysed in terms of clustering the noise levels in the different sensor locations, in order to optimize the spatial distribution of noise monitoring stations. Preliminary studies described in [24; 25] introduced a first statistical approach to the categorization of Milan's District 9 roads, based on the clustering of 14-hourly \(L_{Aeph}\), with the aim of progressing further in the classification than the information given by the legislative road categorization. Moreover, in [26], the authors describe another statistical clustering approach, where the roads having similar flow conditions--and hence similar noise trends due to road traffic--are grouped together, based on an extensive measurement campaign. The authors conclude that two clusters describe the roads in Milan more efficiently than the road categorisation used by the administration. More recent research in the same urban environment has widened the study to use up to 90 sensor locations to represent noise events The proposal was to cluster the sensors by means of the similarities among them. The data used to evaluate the clustering were the equivalent level \(L_{Aeph}\) and the intermittency ratio (IR), which is a metric that reflects the short-time variations of noise exposure [27], of all the sensor locations, the resulting clusters being highly related to the average day-time hourly traffic flow of vehicles. The authors of [28] managed to improve the accuracy of the noise map generation by means of the information given by each and every sensor location, considering the cluster to which each sensor belonged.