Characterization of a WASN-Based Urban Acoustic Dataset for the Dynamic Mapping of Road Traffic Noise

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.

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Detecting Anomalous Noise Events on Low-Capacity Acoustic Sensor in Dynamic Road Traffic Noise Mapping

Proceedings ◽

10.3390/ecsa-4-04897 ◽

2018 ◽

Vol 2 (3) ◽

pp. 136 ◽

Cited By ~ 2

Author(s):

Rosa Alsina-Pagès ◽

Joan Socoró ◽

Francesc Alías

Keyword(s):

Road Traffic ◽

Traffic Noise ◽

Road Traffic Noise ◽

Acoustic Sensor ◽

Noise Mapping

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

Sensors ◽

10.3390/s20174760 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4760

Author(s):

Francesc Alías ◽

Joan Claudi Socoró ◽

Rosa Ma Alsina-Pagès

Keyword(s):

Urban Areas ◽

Road Traffic ◽

Low Cost ◽

Traffic Noise ◽

Road Traffic Noise ◽

Noise Mapping ◽

General Terms ◽

Acoustic Sensor Networks ◽

Time Period ◽

Acoustic Environments

In addition to air pollution, environmental noise has become one of the major hazards for citizens, being Road Traffic Noise (RTN) as its main source in urban areas. Recently, low-cost Wireless Acoustic Sensor Networks (WASNs) have become an alternative to traditional strategic noise mapping in cities. In order to monitor RTN solely, WASN-based approaches should automatize the off-line removal of those events unrelated to regular road traffic (e.g., sirens, airplanes, trams, etc.). Within the LIFE DYNAMAP project, 15 urban Anomalous Noise Events (ANEs) were described through an expert-based recording campaign. However, that work only focused on the overall analysis of the events gathered during non-sequential diurnal periods. As a step forward to characterize the temporal and local particularities of urban ANEs in real acoustic environments, this work analyses their distribution between day (06:00–22:00) and night (22:00–06:00) in narrow (1 lane) and wide (more than 1 lane) streets. The study is developed on a manually-labelled 151-h acoustic database obtained from the 24-nodes WASN deployed across DYNAMAP’s Milan pilot area during a weekday and a weekend day. Results confirm the unbalanced nature of the problem (RTN represents 83.5% of the data), while identifying 26 ANE subcategories mainly derived from pedestrians, animals, transports and industry. Their presence depends more significantly on the time period than on the street type, as most events have been observed in the day-time during the weekday, despite being especially present in narrow streets. Moreover, although ANEs show quite similar median durations regardless of time and location in general terms, they usually present higher median signal-to-noise ratios at night, mainly on the weekend, which becomes especially relevant for the WASN-based computation of equivalent RTN levels.

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

Sensors ◽

10.3390/s20030609 ◽

2020 ◽

Vol 20 (3) ◽

pp. 609 ◽

Cited By ~ 2

Author(s):

Francesc Alías ◽

Ferran Orga ◽

Rosa Ma Alsina-Pagès ◽

Joan Claudi Socoró

Keyword(s):

Sensor Networks ◽

Road Traffic ◽

Traffic Noise ◽

Noise Pollution ◽

Environmental Noise ◽

Integration Time ◽

Road Traffic Noise ◽

Noise Levels ◽

Noise Mapping ◽

Acoustic Data

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.

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Road traffic noise disease burden estimates for a model study of varying urban morphology cases

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2359 ◽

2021 ◽

Vol 263 (3) ◽

pp. 3282-3293

Author(s):

Jens Forssén ◽

Andreas Gustafson ◽

Meta Berghauser Pont ◽

Marie Haeger-Eugensson ◽

Christine Achberger ◽

...

Keyword(s):

Disease Burden ◽

Noise Exposure ◽

Road Traffic ◽

Traffic Noise ◽

Urban Morphology ◽

Urban Setting ◽

Road Traffic Noise ◽

Noise Mapping ◽

Direct Exposure ◽

Life Years

For a model set of 31 different building morphologies in an urban setting, road traffic noise exposure has been calculated and analysed. For five of the building morphologies also vegetation surfaces on facades and roofs were studied. Facade exposures were analysed for both smaller (single-sided) flats and larger (floor-through) flats, considering the direct exposure from the roads as well as the non-direct exposure at noise-shielded positions like inner yards, applying a noise mapping software in combination with a prediction model for the non-direct exposure. Using noise indicators Lden and Lnight, the disease burden, in terms of DALY (Disability-Adjusted Life Years) per person, was estimated and analysed, via predictions of annoyance and sleep disturbance. The resulting effects of varying the building morphology and adding vegetation are shown and discussed, including effects of a bonus model for flats having additional facade elements with lower noise exposure.

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Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities

Journal of Sensors ◽

10.1155/2019/7634860 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 15

Author(s):

Francesc Alías ◽

Rosa Ma. Alsina-Pagès

Keyword(s):

Sensor Networks ◽

Urban Areas ◽

Negative Impact ◽

Smart Cities ◽

Public Awareness ◽

Environmental Noise ◽

Acoustic Sensor ◽

Noise Mapping ◽

Acoustic Sensor Networks ◽

Internet Of Things Technology

Nowadays, more than half of the world’s population lives in urban areas. Since this proportion is expected to keep rising, the sustainable development of cities is of paramount importance to guarantee the quality of life of their inhabitants. Environmental noise is one of the main concerns that has to be addressed, due to its negative impact on the health of people. Different national and international noise directives and legislations have been defined during the past decades, which local authorities must comply with involving noise mapping, action plans, policing, and public awareness, among others. To this aim, a recent change in the paradigm for environmental noise monitoring has been driven by the rise of Internet of Things technology within smart cities through the design and development of wireless acoustic sensor networks (WASNs). This work reviews the most relevant WASN-based approaches developed to date focused on environmental noise monitoring. The proposals have moved from networks composed of high-accuracy commercial devices to the those integrated by ad hoc low-cost acoustic sensors, sometimes designed as hybrid networks with low and high computational capacity nodes. After describing the main characteristics of recent WASN-based projects, the paper also discusses several open challenges, such as the development of acoustic signal processing techniques to identify noise events, to allow the reliable and pervasive deployment of WASNs in urban areas together with some potential future applications.

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Road traffic noise mapping in the Federal University of Santa Maria, Brazil

The Journal of the Acoustical Society of America ◽

10.1121/1.4933454 ◽

2015 ◽

Vol 138 (3) ◽

pp. 1732-1732 ◽

Cited By ~ 2

Author(s):

Olmiro C. de Souza Neto ◽

Stephan Paul

Keyword(s):

Road Traffic ◽

Traffic Noise ◽

Road Traffic Noise ◽

Noise Mapping ◽

Santa Maria

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Soundscape approach integrating noise mapping techniques: a case study in Brighton, UK

Noise Mapping ◽

10.1515/noise-2015-0001 ◽

2015 ◽

Vol 2 (1) ◽

Cited By ~ 19

Author(s):

Francesco Aletta ◽

Jian Kang

Keyword(s):

Road Traffic ◽

Environmental Noise ◽

Sound Level ◽

Road Traffic Noise ◽

Sound Sources ◽

Noise Mapping ◽

Acoustic Environment ◽

Sound Environment ◽

Sound Maps

AbstractIn the guidelines about the management of areas of good environmental noise quality recently published by the European Environment Agency (EEA) it is suggested to combine different methodologies, like noise mapping, sound level measurements and the soundscape approach. Such a recommendation has started to be recognised by a number of local authorities in Europe that are gradually integrating a holistic concept into their environmental noise policies. This research aimed to explore and demonstrate the possibility to integrate conventional noise mapping methods and soundscape methods in an actual urban redevelopment project. A case study was made using the Valley Gardens project in Brighton & Hove (UK). Different scenarios of sound-pressure level distributionswere simulated for both traffic sound sources (i.e. noise maps) and natural sound sources (i.e. sound maps). Additionally, individual responses about the sound environment of the place collected during an on-site question survey were used to implement soundscape maps.The overall picture revealed that the road traffic noise should be reduced, but also it is feasible that preferred sounds likewater features or birdsong could be introduced to make the sound environment more appropriate for the place. Generally, within the framework of this research, noise maps, sound maps and soundscape maps were used together to "triangulate" different layers of information related to the acoustic environment and the way it is perceived, providing a possible working procedure to consider for planners and policy-makers in the future.

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ConvTasNet-based anomalous noise separation for intelligent noise monitoring

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2035 ◽

2021 ◽

Vol 263 (4) ◽

pp. 2044-2051

Author(s):

Han Li ◽

Kean Chen ◽

Bernhard U. Seeber

Keyword(s):

Road Traffic ◽

Low Cost ◽

Traffic Noise ◽

Real Life ◽

Noise Pollution ◽

Road Traffic Noise ◽

Scale Invariant ◽

Acoustic Sensor Networks ◽

Noise Assessment ◽

Sound Separation

Noise pollution has become a growing concern in public health. The availability of low-cost wireless acoustic sensor networks permits continuous monitoring of noise. However, real acoustic scenes are composed of irrelevant sources (anomalous noise) that overlap with monitored noise, causing biased evaluation and controversy. One classical scene is selected in our study. For road traffic noise assessment, other possible non-traffic noise (e.g., speech, thunder) should be excluded to obtain a reliable evaluation. Because anomalous noise is diverse, occasional, and unpredictable in real-life scenes, removing it from the mixture is a challenge. We explore a fully convolutional time-domain audio separation network (ConvTasNet) for arbitrary sound separation. ConvTasNet is trained by a large dataset, including environmental sounds, speech, and music over 150 hours. After training, the scale-invariant signal-to-distortion ratio (SI-SDR) is improved by 11.40 dB on average for an independent test dataset. ConvTasNet is next applied to anomalous noise separation of traffic noise scenes. We mix traffic noise and anomalous noise at random SNR between -10 dB to 0 dB. Separation is especially effective for salient and long-term anomalous noise, which smooth the overall sound pressure level curve over time. Results emphasize the importance of anomalous noise separation for reliable evaluation.

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