Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

Abstract

Environmental noise can be defined as the accumulation of noise pollution caused by sounds generated by outdoor human activities, Road Traffic Noise (RTN) being the main source in urban and suburban areas. To address the negative effects of environmental noise on public health, the European Environmental Noise Directive requires EU member states to tailor noise maps and define the corresponding action plans every five years for major agglomerations and key infrastructures. Noise maps have been hitherto created from expert-based measurements, after cleaning the recorded acoustic data of undesired acoustic events, or Anomalous Noise Events (ANEs). In recent years, Wireless Acoustic Sensor Networks (WASNs) have become an alternative. However, most of the proposals focus on measuring global noise levels without taking into account the presence of ANEs. The LIFE DYNAMAP project has developed a WASN-based dynamic noise mapping system to analyze the acoustic impact of road infrastructures in real time based solely on RTN levels. After studying the bias caused by individual ANEs on the computation of the A-weighted equivalent noise levels through an expert-based dataset obtained before installing the sensor networks, this work evaluates the aggregate impact of the ANEs on the RTN measurements in a real-operation environment. To that effect, 304 h and 20 min of labeled acoustic data collected through the two WASNs deployed in both pilot areas have been analyzed, computing the individual and aggregate impacts of ANEs for each sensor location and impact range (low, medium and high) for a 5 min integration time. The study shows the regular occurrence of ANEs when monitoring RTN levels in both acoustic environments, which are especially common in the urban area. Moreover, the results reveal that the aggregate contribution of low- and medium-impact ANEs can become as critical as the presence of high-impact individual ANEs, thus highlighting the importance of their automatic removal to obtain reliable WASN-based RTN maps in real-operation environments.

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

“…Due to the nature of the project, that consisted in removing events not related to traffic noise from the noise map computation, events were grouped in RTN (Road Traffic Noise) that belongs to the 83.7% of the total time of the dataset, and ANE (Anomalous Noise Event) with the 8.7% of the total time. Another class was used to include overlapping and unidentified events: COMPLX (complex) with 7.6% of the total time [20]. During the labelling process, the DYNAMAP developers found up to 26 types of anomalous events, which they decided to group in the following classes: airplane, alarm, bell, bike, bird, blind, brake, bus door, construction, dog, door, glass, horn, interference, music, people, rain, rubbish service, siren, squeak, step, thunder, tramway, train, trolley, wind, works (construction) [35].…”

“…The most representative audio excerpts were selected, using a wide range of sound types (sirens, airplanes, people talking, dogs barking, etc.) [20,21], keeping the constants of location and sensor calibration. However, sound annoyance depends on the acoustic characterization of each sample, and it is possible to classify the acoustic excerpts depending on their characterization, which can be the basis to ask participants about their perceptions.…”

WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

“…A possible explanation to this difference is that the expert-based dataset recording was centred in day-time while the WASN-based database included also nocturnal samples, which showed a lower presence of ANEs. A complementary analysis to these works can be found in [ 38 ], which was focused on evaluating the aggregate impact of the ANEs occurring in the acoustic environments on the computation of values. Nevertheless, none of these previous works developed within the DYNAMAP project pretended to study the temporal evolution of the characteristics describing ANEs at specific locations as they were analysed in an aggregate manner.…”

BCNDataset: Description and Analysis of an Annotated Night Urban Leisure Sound Dataset

“…A possible explanation to these differences is that the expert-based dataset recording was centered in daytime, and this WASN-based dataset was recorded day and night, where night shows low presence of ANEs with respect to the day. A complementary analysis to these works can be found in [31], which is focused on evaluating the aggregate impact of the ANEs occurring in the acoustic environments where the sensors of WASNs are installed.…”