**Rosa Ma Alsina-Pagès \*, Ferran Orga, Francesc Alías and Joan Claudi Socoró**

GTM—Grup de recerca en Tecnologies Mèdia, La Salle—Universitat Ramon Llull., C/Quatre Camins, 30, 08022 Barcelona, Spain; ferran.orga@salle.url.edu (F.O.); francesc.alias@salle.url.edu (F.A.); joanclaudi.socoro@salle.url.edu (J.C.S.)

**\*** Correspondence: rosamaria.alsina@salle.url.edu; Tel.: +34-932-902-455

Received: 29 March 2019; Accepted: 27 May 2019; Published: 30 May 2019

**Abstract:** Traffic noise is presently considered one of the main pollutants in urban and suburban areas. Several recent technological advances have allowed a step forward in the dynamic computation of road-traffic noise levels by means of a Wireless Acoustic Sensor Network (WASN) through the collection of measurements in real-operation environments. In the framework of the LIFE DYNAMAP project, two WASNs have been deployed in two pilot areas: one in the city of Milan, as an urban environment, and another around the city of Rome in a suburban location. For a correct evaluation of the noise level generated by road infrastructures, all Anomalous Noise Events (ANE) unrelated to regular road-traffic noise (e.g., sirens, horns, speech, etc.) should be removed before updating corresponding noise maps. This work presents the production and analysis of a real-operation environmental audio database collected through the 19-node WASN of a suburban area. A total of 156 h and 20 min of labeled audio data has been obtained differentiating among road-traffic noise and ANEs (classified in 16 subcategories). After delimiting their boundaries manually, the acoustic salience of the ANE samples is automatically computed as a contextual Signal-to-Noise Ratio (SNR) together with its impact on the A-weighted equivalent level (Δ*LAeq*). The analysis of the real-operation WASN-based environmental database is evaluated with these metrics, and we conclude that the 19 locations of the network present substantial differences in the occurrences of the subcategories of ANE, with a clear predominance of the noise of sirens, trains, and thunder.

**Keywords:** road-traffic noise; anomalous noise event; acoustic dataset; noise monitoring; smartcity; WASN; SNR; impact; *LAeq*; urban sound; noise maps
