## 1 Introduction  
Community noise (also called environmental, urban, or residential noise) is one of the main problems that cause a negative impact on health. Since the 1990s, the World Health Organization (WHO) has developed different guidelines to serve as a basis for the standards in the management of environmental noise. In the European Union, since the publication of the Environmental Noise Directive 2002/49/EC (END) [1], the European Member States have developed their own legislation to reduce the noise levels to which their populations are exposed. Although considerable efforts have been made to reduce noise levels, the latest report published by the European Environmental Agency [2] indicates that this has not been accomplished. This concern to control and manage noise pollution in general, has resulted in the advent of smart cities, which have boosted the deployment of monitoring systems. Monitoring systems are effective tools for the evaluation and management of acoustic pollution, which complement noise strategic maps, the main tool currently in use. Noise maps have certain limitations for noise-source assessment: only a few noise sources are taken into account and they use averaged data over long periods for calculating the acoustic emission of the sources. The use of monitoring systems would overcome those limitations and contribute to obtain a more realistic approximation of noise levels, especially in cities. The data provided using real-time monitoring systems allow other types of analysis, such as the characterization of behavioural patterns and detection of high-level short-duration noise events that can cause annoyance and negative health effects. However, current monitoring systems have the downside of not identifying the sound sources that generate the noises, making it difficult to take corrective actions. This problem aroosed in one of the earliest research projects on the use of sensors in a continuous monitoring system for evaluating noise pollution, conducted in Palma de Mallorca [3]. This project's objective was to assess how sound pressure levels (SPL) and particulate matter (PM10) particles produced by port activity were related to one another. They were unable to establish a clear connection between the noise levels recorded and the noise sources responsible for them. Sound-classification techniques can be used to solve this issue, making it possible to identify the noise source responsible for a specific sound event. New approaches to age-old problems in the analysis and processing of sound signals, like speech recognition and sound categorization, have been made possible by the application of machine learning techniques. Machine learning is a set of techniques that allow computer programs to automatically improve doing a task through experience. These systems are able to extract and identify complex patterns in the data (in our case sound) that could not be processed using traditional processing and handcrafted features. More specifically, the term Machine Listening or Machine Audition [4], encompasses the study of techniques and systems that allow computers to automatically identify sounds as humans do. There are many uses for machine listening, but two of the most important ones are the sound event detection [5], which identifies the beginning and end of a sound event in a recording, and audio classification which aims to categorize or label a recording. There are many application fields for audio classification, including the categorization of environmental sounds (both urban and nature) [6], bioacoustics signal classification and detection [7; 8] and the classification of urban sound events in noise monitoring systems [9; 10; 11], our research topic. A lot of the research in this area has been promoted by the Detection and Classification of Acoustic Scenes and Events (DCASE) community, which has been organising challenges and workshops for the last years.