## 1 Introduction  
Traffic noise is one of the main pollutants in urban and suburban areas that affects the quality of life of their citizens. As cities grow in size and population, the consequent increase in traffic is making this problem even more present and bothersome. In order to address this issue, European authorities have driven several initiatives to study, prevent and reduce the effects of exposure of population to traffic noise. Among them, the European Noise Directive (END) [1] is focused on the creation of noise level maps in order to inform citizens of their exposure to noise, besides drawing up appropriate action plans to reduce its negative impact. In general terms, these maps represent the equivalent noise level (\(L_{eq}\)) and are updated every five years [1]. This is costly and time-consuming process that is undertaken by local and regional governments, and the resulting action plans can only be implemented and evaluated every five years. These noise maps have been historically collected and generated by means of costly expert measurements using certified devices, based on short term periods that try to be as much representative as possible. However, this classic approach has recently undergone a dramatic change of paradigm thanks to the emergence of the so-called Wireless Acoustic Sensor Networks (WASNs) [2]. The WASN have been developed under the paradigms of both the Smart City and the Internet-of-Things. In literature, we can find different WASN designed and deployed for several outdoor applications, some of which focus on security and surveillance purposes through the identification and localization of specific sounds related to hazardous situations. For instance, in [3], an ad-hoc WASN was designed to detect and locate shots for sniper detection. More recently, in [4] a WASN based on the FIWARE platform was deployed to locate and identify a broader type of sound sources such as people screaming or talking loudly, shot guns, horns, road accidents, etc. by including ambisonic microphones in the network. Finally, although no specific WASN is explicitly described, the proposal described in [5] is focused on the detection of road traffic accidents through the acoustic identification of tire skidding and car crashes. Moreover, WASNs have also been deployed for city noise management, which involves noise mapping, action plans development, policing and improving public awareness, among others [6]. For instance, the SENSEable project [7] proposed a WASN to collect information about the acoustic environment of the city of Pisa (Italy) using low-cost acoustic sensors to study the relationship between public health, mobility and pollution through the analysis of citizens' behaviour. Projects which have adopted a quite similar approach include the IDEA project in Belgium [8], the RUMEUR network in France [9] with a specific focus on aircraft noise, the 'Barcelona noise monitoring network' that is integrated in the _Sentillo_ city management platform in Spain [10], or the DYNAMAP project, which is aimed at developing a dynamic noise mapping system able to detect and represent in real time the acoustic impact of road infrastructures in the cities of Rome and Milan (Italy) [11]. Nevertheless, the WASN paradigm poses several challenges, ranging from those derived from the design and development of the wireless sensor network itself [12; 13], e.g., energy harvesting and low-cost hardware development and maintenance, to some specific challenges derived from the automation of the data collection and subsequent signal processing (see [2; 14] and references therein). For instance, if the WASN is explicitly designed to measure road traffic noise levels, any acoustic event produced by any other noise source that could alter the \(L_{eq}\) measure (e.g., an air-craft flying over, nearby railways, road works, bells, crickets, etc.) should be detected and removed from the map computation to provide a reliable picture of the road noise level.