## 1 Introduction  
Sound waves, or noise emissions, are one of the pollutants that urban citizens are most concerned about [1]. To identify, measure, and determine exposure to environmental noise, city rulers are developing data strategies to capture, transform, and analyze information using Internet of Things (IoT) and big data technologies. European Directive 2002/49/EC aims to establish a common approach for the assessment and management of environmental noise in order to standardize procedures and metrics. The goal is to avoid, prevent, and reduce harmful effects, including annoyance, for citizens as a result of exposure to different noise sources [2]. The directive specifically promotes agglomerations of people such as cities or clusters of cities to create strategic noise mapping (SNM) and then share the findings with the public. Additionally, the outcome of these noise maps has led to the formation of action plans for noise reduction in areas identified as having high noise exposure (noise exposure protection zones). More recently, numerous large cities have begun to deploy Wireless Acoustic Sensor Networks (WASN), which are based on IoT technologies [3], in order to gather noise data that can be analyzed and utilized to update SNM and action plans. These WASNs are usually made up of two different types of stations: fixed-location sensors for long-term monitoring, and temporal location sensors for short-term monitoring. The latter can take the form of temporarily deployed sensors, instrumented vehicles with an acoustic sensor together with a geopositioning system to locate the measurement, or regular sound measure devices known as sonometers [4]. While fixed stations remain at one place all their lifetime, allowing the continuous monitoring of noise levels to identify trends and seasonality, temporal stations are placed in a particular site during an established period of time (minutes, hours, or days) to measure the acoustic soundfield by gathering short-term data. To recognize environmental acoustic patterns or behaviors, using the average or median of noise indicators for the overall assessment period, generally at least one year, is recommended by the Directive [2]. Therefore, short-term data are not usually considered due to the lack of capability to capture seasonality components such as holidays or weekends. After the analysis of previously enunciated long-term statistics, two principal types of environmental acoustic patterns are usually recognized: special regime areas and quiet areas. Special regime areas include locations where the noise indicator exceeds a high threshold, while quiet areas include locations where the noise indicator is below a low threshold. Although other patterns with complex behavior can exist, advanced statistical techniques are required to recognize them. In a number of out prior works [5; 6], we applied unsupervised learning techniques to group the nodes of a WASN in clusters with the same behavior and recognize complex patterns on this basis. These complex patterns can provide insights to city managers for establishing personalized strategies and defining new acoustic areas. In the current research, the application of a supervised machine learning technique, Artificial Neural Network (ANN), is proposed to predict the long-term acoustic behavior group to which a location belongs by means of short-term measurements. In this way, temporal stations can be used by city managers to identify the environmental acoustic pattern of a site, enhancing the value of the WASN and improving the SNM. During last few years, machine learning algorithms have been considered in a number of studies involving environmental acoustic data captured by WASNs. Many of the studies found in the literature use supervised machine learning approaches to analyze audio signals. In New York City, a comprehensive dataset [7] of labeled audio recordings was generated utilizing a WASN [8] for the design and assessment of machine learning techniques.