## 1 Introduction
The detection of sound events is typically based on the segmentation of the input acoustic data into audio chunks that represent a single occurrence of a predefined acoustic class, separating them from other overlapping events if necessary [15], a task that is also denoted in the literature as polyphonic sound event detection, e.g., see [16; 17]. The acoustic event detection and classification is closely related to the so-called computational auditory scene analysis (CASA) paradigm [18], which is devoted to acoustic scene classification and detection of sound events within an acoustic scene or soundscape [15]. These typically take advantage of being trained on specifically designed environmental databases containing the target finite set of acoustic classes (e.g., see [15; 19; 20]). Therefore, it is necessary to design and develop representative audio databases containing this kind of non-desired sounds--hereafter denoted as _anomalous noise events_ (ANE) [5; 21]--to allow the signal processing block to automatically detect and discard them when captured by the WASN. However, the highly local, occasional, diverse and unpredictable nature of the ANE concept [21; 22], together with the naturally unbalanced distribution of acoustic events in real-life environments [20; 23] makes it difficult to build acoustic databases which are representative enough. This is of paramount importance to allow the WASN becoming reliable in front of ANEs, which should take into account the acoustic salience [24; 25; 26] of the anomalous noise event with respect to the background noise (i.e., a salient ANE should be detected and removed from the road traffic noise \(L_{eq}\) computation to avoid biasing the noise map generation). To tackle this problem, several works have tried to build these databases by artificially mixing background noise with ANEs and considering different event-to-noise ratio (hereafter, Signal-to-Noise ratio or SNR) distributions, by mixing real background recordings with event excerpts from online digital repositories [21; 27] or from isolated individual recording of actual sound sources [19; 22]. Although this approach can help to improve the training process [22], it can also yield results that may not represent what is actually found in real-life environments due to acoustic data misrepresentation [28]. This article describes the design, recording campaign and analysis of a real-life environmental audio database of anomalous noise events gathered in the two pilot areas of the DYNAMAP project ([[http://www.life-dynamap.eu](http://www.life-dynamap.eu)]([http://www.life-dynamap.eu](http://www.life-dynamap.eu))) [11]: an urban area within Milan's district 9, and a suburban area along the Rome A90 highway. After manually labelling the collected samples of road traffic noise, background noise and anomalous noise events (subsequently divided into 19 subcategories), the description of the ANE is enriched through the automatic computation of their acoustic salience--defined as the contextual SNR of the event with respect to the traffic or background noise. The paper is completed with a comprehensive discussion on the obtained results that tries to shed light on several key aspects that should be taken into consideration when developing ANE databases for machine hearing approaches in real-life environments, and their implication for the modelling and automatic anomalous noise event recognition, whose development is out of the scope of this work. To our knowledge, no explicit studies have been previously conducted to consider the complexity of real-life soundscapes to provide the deployed WASN with the capability to discard ANEs from traffic noise map computation. This paper is structured as follows. The work related to the most relevant previous attempts to generate environmental audio databases is explained in Section 2.