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Sound Monitoring Acoustic Sensor Network Design for Urban and Suburban Environments

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 19061

Special Issue Editors

Dr. Rosa Ma Alsina-Pagès

E-Mail Website
Guest Editor
GTM—Grup de recerca en Tecnologies Mèdia, La Salle—Universitat Ramon Llull, c/Quatre Camins, 30, 08022 Barcelona, Spain
Interests: acoustic event detection; real-time signal processing; adaptive signal processing; noise monitoring; noise annoyance; impact of noise events
Special Issues, Collections and Topics in MDPI journals
Dr. Giovanni Zambon

E-Mail Website
Guest Editor
Department of Earth and Environmental Sciences, Università degli Studi di Milano-Bicocca, Milan, Italy
Interests: soundscape in urban and natural areas; eco-acoustics; soundscape in marine habitats; noise mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Environmental Noise Directive (END) requires that a five-year updating of noise maps is carried out, to check and report on changes that have occurred during the reference period. This led last year’s END to deploy several wireless acoustic sensor networks to improve evaluation of the impact of road traffic noise in the cities around the world. Nevertheless, the END opens the door to the analysis of sound taking into account its source. Annoyance is closely related to both the LAeq value (the equivalent value) of a sound and the type of sound (e.g., road traffic noise, music, birdsong, sirens, alarms, works...). Thus, a new generation of acoustic sensor networks should be designed, in order to come a step closer to sound mapping. So far, several noise mapping sensors, networks and platforms have been developed and deployed in some cities and suburban environments. This new approach is devoted to sound, and not just noise (which is usually limited to non-desired sounds). This new wireless acoustic sensor network requires broad knowledge in several disciplines: accurate hardware design for the acoustic sensors; artificial intelligence algorithms to differentiate the sources of noise; network structure design; information management, and graphical user interface design to communicate the results to users. This Special Issue focuses on all the technologies necessary for development of an efficient wireless acoustic sensor network, from the early design stages through to deployment testing, performance, and policy implications. This Special Issue, prepared by two guest editors, describes the latest trends in worldwide WASN design projects aimed at the design and implementation of smart acoustic sensor networks. The focus of the contributions is on good practice, suitable for the design and deployment of intelligent networks in other locations.

Dr. Rosa Ma Alsina-Pagès
Dr. Giovanni Zambon
Guest Editors

Manuscript Submission Information

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Published Papers (5 papers)

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Research

23 pages, 27935 KiB  
Article
Mapping of the Acoustic Environment at an Urban Park in the City Area of Milan, Italy, Using Very Low-Cost Sensors
by Roberto Benocci, Andrea Potenza, Alessandro Bisceglie, Hector Eduardo Roman and Giovanni Zambon
Sensors 2022, 22(9), 3528; https://doi.org/10.3390/s22093528 - 6 May 2022
Cited by 15 | Viewed by 2672
Abstract
The-growing influence of urbanisation on green areas can greatly benefit from passive acoustic monitoring (PAM) across spatiotemporal continua to provide biodiversity estimation and useful information for conservation planning and development decisions. The capability of eco-acoustic indices to capture different sound features has been [...] Read more.
The-growing influence of urbanisation on green areas can greatly benefit from passive acoustic monitoring (PAM) across spatiotemporal continua to provide biodiversity estimation and useful information for conservation planning and development decisions. The capability of eco-acoustic indices to capture different sound features has been harnessed to identify areas within the Parco Nord of Milan, Italy, characterised by different degrees of anthropic disturbance and biophonic activity. For this purpose, we used a network of very low-cost sensors distributed over an area of approximately 20 hectares to highlight areas with different acoustic properties. The audio files analysed in this study were recorded at 16 sites on four sessions during the period 25–29 May (2015), from 06:30 a.m. to 10:00 a.m. Seven eco-acoustic indices, namely Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bio-Acoustic Index (BI), Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NSDI), and Dynamic Spectral Centroid (DSC) were computed at 1 s integration time and the resulting time series were described by seven statistical descriptors. A dimensionality reduction of the indices carrying similar sound information was obtained by performing principal component analysis (PCA). Over the retained dimensions, describing a large (∼80%) variance of the original variables, a cluster analysis allowed discriminating among sites characterized by different combination of eco-acoustic indices (dimensions). The results show that the obtained groups are well correlated with the results of an aural survey aimed at determining the sound components at the sixteen sites (biophonies, technophonies, and geophonies). This outcome highlights the capability of this analysis of discriminating sites with different environmental sounds, thus allowing to create a map of the acoustic environment over an extended area. Full article
(This article belongs to the Special Issue Sound Monitoring Acoustic Sensor Network Design for Urban and Suburban Environments)
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31 pages, 15934 KiB  
Article
Evaluation of Acoustic Noise Level and Impulsiveness Inside Vehicles in Different Traffic Conditions
by Daniel Flor, Danilo Pena, Hyago Lucas Oliveira, Luan Pena, Vicente A. de Sousa, Jr. and Allan Martins
Sensors 2022, 22(5), 1946; https://doi.org/10.3390/s22051946 - 2 Mar 2022
Cited by 1 | Viewed by 2503
Abstract
Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle [...] Read more.
Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise. Full article
(This article belongs to the Special Issue Sound Monitoring Acoustic Sensor Network Design for Urban and Suburban Environments)
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22 pages, 30693 KiB  
Article
Multilabel Acoustic Event Classification Using Real-World Urban Data and Physical Redundancy of Sensors
by Ester Vidaña-Vila, Joan Navarro, Dan Stowell and Rosa Ma Alsina-Pagès
Sensors 2021, 21(22), 7470; https://doi.org/10.3390/s21227470 - 10 Nov 2021
Cited by 12 | Viewed by 2282
Abstract
Many people living in urban environments nowadays are overexposed to noise, which results in adverse effects on their health. Thus, urban sound monitoring has emerged as a powerful tool that might enable public administrations to automatically identify and quantify noise pollution. Therefore, identifying [...] Read more.
Many people living in urban environments nowadays are overexposed to noise, which results in adverse effects on their health. Thus, urban sound monitoring has emerged as a powerful tool that might enable public administrations to automatically identify and quantify noise pollution. Therefore, identifying multiple and simultaneous acoustic sources in these environments in a reliable and cost-effective way has emerged as a hot research topic. The purpose of this paper is to propose a two-stage classifier able to identify, in real time, a set of up to 21 urban acoustic events that may occur simultaneously (i.e., multilabel), taking advantage of physical redundancy in acoustic sensors from a wireless acoustic sensors network. The first stage of the proposed system consists of a multilabel deep neural network that makes a classification for each 4-s window. The second stage intelligently aggregates the classification results from the first stage of four neighboring nodes to determine the final classification result. Conducted experiments with real-world data and up to three different computing devices show that the system is able to provide classification results in less than 1 s and that it has good performance when classifying the most common events from the dataset. The results of this research may help civic organisations to obtain actionable noise monitoring information from automatic systems. Full article
(This article belongs to the Special Issue Sound Monitoring Acoustic Sensor Network Design for Urban and Suburban Environments)
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22 pages, 6501 KiB  
Article
Validation of a Low-Cost Pavement Monitoring Inertial-Based System for Urban Road Networks
by Giuseppe Loprencipe, Flavio Guilherme Vaz de Almeida Filho, Rafael Henrique de Oliveira and Salvatore Bruno
Sensors 2021, 21(9), 3127; https://doi.org/10.3390/s21093127 - 30 Apr 2021
Cited by 22 | Viewed by 4168
Abstract
Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, a novel inertial sensor-based system is proposed using a low-cost inertial measurement unit (IMU) and a global positioning system [...] Read more.
Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, a novel inertial sensor-based system is proposed using a low-cost inertial measurement unit (IMU) and a global positioning system (GPS) module, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to indirectly monitor the road condition. To assess the level of pavement decay, the comfort index awz defined by the ISO 2631 standard was used. Considering 21 km of roads with different levels of pavement decay, validation measurements were performed using the novel sensor, a high performance inertial based navigation sensor, and a road surface profiler. Therefore, comparisons between awz determined with accelerations measured on the two different inertial sensors are made; in addition, also correlations between awz, and typical pavement indicators such as international roughness index, and ride number were also performed. The results showed very good correlations between the awz values calculated with the two inertial devices (R2 = 0.98). In addition, the correlations between awz values and the typical pavement indices showed promising results (R2 = 0.83–0.90). The proposed sensor may be assumed as a reliable and easy-to-install method to assess the pavement conditions in urban road networks, since the use of traditional systems is difficult and/or expensive. Full article
(This article belongs to the Special Issue Sound Monitoring Acoustic Sensor Network Design for Urban and Suburban Environments)
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21 pages, 5124 KiB  
Article
A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments
by Daniel Bonet-Solà and Rosa Ma Alsina-Pagès
Sensors 2021, 21(4), 1274; https://doi.org/10.3390/s21041274 - 11 Feb 2021
Cited by 30 | Viewed by 5954
Abstract
Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source [...] Read more.
Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora. Full article
(This article belongs to the Special Issue Sound Monitoring Acoustic Sensor Network Design for Urban and Suburban Environments)
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