###### Abstract  
Environmental noise has become one of the principal health risks for urban dwellers and road traffic noise (RTN) is considered to be the main source of these adverse effects. To address this problem, strategic noise maps and corresponding action plans have been developed throughout Europe in recent years in response to the European Noise Directive 2002/49/EC (END), especially in populated cities. Recently, wireless acoustic sensor networks (WASNs) have started to serve as an alternative to static noise maps to monitor urban areas by gathering environmental noise data in real time. Several studies have analysed and categorized the different acoustic environments described in the END (e.g., traffic, industrial, leisure, etc.). However, most of them have only considered the dynamic evolution of the A-weighted equivalent noise levels \(L_{Aeq}\) over different periods of time. In order to focus on the analysis of RTN solely, this paper introduces a clustering methodology to analyse and group spectro-temporal profiles of RTN collected simultaneously across an area of interest. The experiments were conducted on two acoustic databases collected during a weekday and a weekend day through WASNs deployed in the pilot areas of the LIFE+ DYNAMAP project. The results obtained show that the clustering of RTN, based on its spectro-temporal patterns, yields different solutions on weekdays and at weekends in both environments, being larger than those found in the suburban environment and lower than the number of clusters in the urban scenario. road traffic noise; spectro-temporal analysis; WASN; clustering +
Footnote †: journal: Medical Imaging  
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Footnote 1: email: _Applied_  
Footnote †: journal: Medical Imaging  
area, and another on the outskirts of Rome as a suburban environment. Specifically, the urban WASN is composed of 24 acoustic nodes installed in different facades of public buildings across District 9 of Milan [10], while the suburban WASN is composed of 19 acoustic sensing nodes placed in the portals of the A90 motorway surrounding Rome [11]. For the proper computation of the equivalent noise levels (\(L_{eq}\)) of road infrastructures, acoustic events unrelated to regular road traffic noise (RTN), (considered as those which come from from vehicles engines and the contact between their types and the pavement [7]), should be removed automatically to avoid biasing the RTN map generation. These events are denoted as anomalous noise events (ANEs) within the DYNAMAP project, and represent, for instance, trains, airplanes, sirens, horns, speech, doors, works, etc. To this end, an anomalous noise events detector (ANED) has been designed as a two-class classifier (ANE vs. RTN) using mel-frequency cepstral coefficients (MFCC) [12] and Gaussian mixture models, and has been implemented in the low-cost sensors running locally in real-time [13,14]. In order to train the ANED algorithm properly, several previous studies have focused on the collection, characterization and impact analysis of ANEs in the A-weighted environmental noise levels (\(L_{Aeq}\)) computation (see e.g., [10,11,15]). However, much less attention has been paid to the majority class, that is, RTN, which represents around 90% of the collected data [15], which is also relevant, since spectral components and evolution of the road traffic noise of each specific location may also affect the performance of the ANED algorithm. Several investigations have addressed the study of the similarities and differences of acoustic environments by means of clustering techniques, using data collected through WASNs. Most of them consider \(L_{Aeq}\) values computed, using several window spans, depending on the goal of each analysis, in the deployed sensors, as well as their temporal evolution during the day and on different days of the week. Among them, it is worth mentioning that several works have analysed Milan's DYNAMAP urban environment to cluster that neighborhood through different approaches based on \(L_{Aeq}\) measurements.