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Road Traffic Noise (RTN) is one of the main pollutants in urban and suburban areas, negatively affecting the quality of life of their inhabitants. In the context of the European LIFE DYNAMAP project, two Wireless Acoustic Sensor Networks (WASN) have been deployed to monitor RTN: one in District 9 of Milan, and another along the A90 motorway of Rome. Since the dynamic mapping system should be able to identify and remove those Anomalous Noise Events (ANEs) unrelated to regular road traffic (e.g., sirens, horns, speech, and doors), an Anomalous Noise Event Detector (ANED) has been included in the dynamic noise mapping pipeline to avoid biasing the computation of the equivalent RTN levels. After deploying the 24 low-cost acoustic sensor networks in both pilot areas, WASN-based acoustic datasets were built to adapt the previous version of the ANED algorithm to run in real-operation conditions. In this work, we describe the preliminary results of the analysis of the 154 h WASN-based urban acoustic dataset obtained from the Milan city in terms of the main characteristics of ANEs. The results confirm the unbalanced nature of the problem (83.7% of the data corresponds to RTN), showing the urban WASN-based dataset a larger number of ANEs with higher local predominance than what was observed in the previous expert-based recording campaign, which underlines the importance of the accurate modeling of the urban acoustic environment to train the ANED properly.

City noise and sound are measured and processed with the purpose of drawing appropriate government legislation and regulations, ultimately aimed at contributing to a healthier environment for humans. The primary use of urban noise analysis is carried out with the main purpose of reporting or denouncing, to the appropriate authorities, a misconduct or correct a misuse of council resources. We believe that urban sounds carry more information than what it is extracted to date. In this paper we present a cloud-based urban sound analysis system for the capturing, processing and trading of urban sound-based information. By leveraging modern artificial intelligence algorithms running on a FOG computing city infrastructure, we will show how the presented solution can offer a valuable solution for exploiting urban sound information. A specific focus is given to the hardware implementation of the sound sensor and its multimicrophone architecture. We discuss how the presented architecture is designed to allow the trading of sound information between independent parties, transparently, using cloud-based sound processing APIs running on an inexpensive consumer-grade microphone.

The DYNAMAP project is aimed at implementing a dynamic noise mapping system able to determine the acoustic impact of road infrastructures in real time. It was established as a result of the European Noise Directive 2002/49/EC. A Wireless Acoustic Sensor Network (WASN) is used to collect the measurements in two pilot areas: in the city of Milan (urban) and on the A90 motorway around Rome (suburban). For a proper evaluation of the noise level of the road infrastructures, the anomalous noise events (ANE) unrelated to traffic noise (e.g., sirens, horns, speech, doors, etc.) should be removed before updating the noise maps. For this purpose, an anomalous noise events detector (ANED) was initially designed and trained using data from a real-life recording campaign. In this work, we continue adapting the ANED algorithm to conform to the requirements of the final 25-node hybrid WASN deployed in the suburban environment. Specifically, the study focuses on the analysis of the spectro-temporal characteristics of the acoustic data in real-operation conditions and their differences between the 19 high-capacity nodes in the Rome pilot area in order to adapt the ANED to run on the entire WASN appropriately.