Sons al Balcó: A Comparative Analysis of WASN-Based LAeq Measured Values with Perceptual Questionnaires in Barcelona during the COVID-19 Lockdown.

The mobility and activity restrictions imposed in Spain due to the COVID-19 pandemic caused a significant improvement in the urban noise pollution that could be objectively measured in those cities with acoustic sensor networks deployed. This significant change in the urban soundscapes was also perceived by citizens who positively appraised this new acoustic scenario. In this work, authors present a comparative analysis between different noise indices provided by 70 sound sensors deployed in Barcelona, both during and before the lockdown, and the results of a perceptual test conducted in the framework of the project Sons al Balcó during the lockdown, which received more than one hundred contributions in Barcelona alone. The analysis has been performed by clustering the objective and subjective data according to the predominant noise sources in the location of the sensors and differentiating road traffic in heavy, moderate and low-traffic areas. The study brings out strong alignments between a decline in noise indices, acoustic satisfaction improvement and changes in the predominant noise sources, supporting the idea that objective calibrated data can be useful to make a qualitative approximation to the subjective perception of urban soundscapes when further information is not available.

Analysis and Acoustic Event Classification of Environmental Data Collected in a Citizen Science Project.

Citizen science can serve as a tool to obtain information about changes in the soundscape. One of the challenges of citizen science projects is the processing of data gathered by the citizens, to obtain conclusions. As part of the project Sons al Balcó, authors aim to study the soundscape in Catalonia during the lockdown due to the COVID-19 pandemic and afterwards and design a tool to automatically detect sound events as a first step to assess the quality of the soundscape. This paper details and compares the acoustic samples of the two collecting campaigns of the Sons al Balcó project. While the 2020 campaign obtained 365 videos, the 2021 campaign obtained 237. Later, a convolutional neural network is trained to automatically detect and classify acoustic events even if they occur simultaneously. Event based macro F1-score tops 50% for both campaigns for the most prevalent noise sources. However, results suggest that not all the categories are equally detected: the percentage of prevalence of an event in the dataset and its foregound-to-background ratio play a decisive role.

Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring.

This work presents the design of a wireless acoustic sensor network (WASN) that monitors indoor spaces. The proposed network would enable the acquisition of valuable information on the behavior of the inhabitants of the space. This WASN has been conceived to work in any type of indoor environment, including houses, hospitals, universities or even libraries, where the tracking of people can give relevant insight, with a focus on ambient assisted living environments. The proposed WASN has several priorities and differences compared to the literature: (i) presenting a low-cost flexible sensor able to monitor wide indoor areas; (ii) balance between acoustic quality and microphone cost; and (iii) good communication between nodes to increase the connectivity coverage. A potential application of the proposed network could be the generation of a sound map of a certain location (house, university, offices, etc.) or, in the future, the acoustic detection of events, giving information about the behavior of the inhabitants of the place under study. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally following the edge-computing paradigm to avoid sending raw data to a cloud server, mainly for privacy and connectivity purposes. Moreover, this work explores the placement of acoustic sensors in a real scenario, following acoustic coverage criteria. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioral and environmental information, in order to take decisions in real-time with the minimum intrusiveness in the location under study.

Effects of COVID-19 lockdown in Milan urban and Rome suburban acoustic environments: Anomalous noise events and intermittency ratio.

The COVID-19 pandemic affected the acoustic environment worldwide, entailing relevant reductions of equivalent noise levels (LAeq) during this exceptional period. In the context of the LIFE+ DYNAMAP project, two wireless acoustic sensor networks were deployed in Milan and Rome. Taking advantage of the built-in identification of anomalous noise events (ANE) in the sensors, this work analyses the effects of the COVID-19 lockdown in both urban and suburban acoustic environments from January to June 2020, considering the distribution of ANEs and the intermittency ratio (IR) as an indicator of the impact of noise on population. The results show statistically significant increments of ANEs in Rome during the lockdown, mainly on weekends, and especially at night, despite the significant decrease in salient events. Differently, ANEs decrease during the lockdown in Milan, mostly at daytime, as a result of population confinement. Although the IR increases in several urban locations, most sensed locations show a relevant decrease in IR during the confinement, which represents a noteworthy reduction of the negative impact of noise in the population of both cities. During the post-lockdown period, all the scores start to return to those observed in the pre-lockdown, but still remaining higher than in 2019.

Multilabel Acoustic Event Classification Using Real-World Urban Data and Physical Redundancy of Sensors.

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.

Changes in the soundscape of Girona during the COVID lockdown.

The lockdown measures in Spain due to COVID-19 social measures showed a wide decrease in the urban noise levels observed. This paper presents an analysis of the noise levels in Girona, a 100 000 citizen city in the North-East of Catalonia (Spain). We present the LAeq levels in four different locations from January 2020 to June 2020, including all the stages of the lockdown. Several comparisons are conducted with the monitoring data available from the previous years (2019, 2018, and 2017, when available). This analysis is part of the project "Sons al Balcó," which aims to draw the soundscape of Catalonia during the lockdown. The results of the analysis in Girona show drastic LAeq changes especially in nightlife areas of the city, moderate LAeq changes in commercial and restaurants areas, and low LAeq changes in dense traffic areas.

The Soundscape of the COVID-19 Lockdown: Barcelona Noise Monitoring Network Case Study.

The lockdown social measures in Spain due to COVID-19 caused a significant decrease in urban noise levels, which was observed in most of the large cities. This paper presents an analysis of the noise levels in Barcelona, Spain, by means of an accurate analysis of the most relevant sensors deployed in the Barcelona Noise Monitoring Network. In this work, we present the LAeq levels in eight different locations from January 2020 to June 2020-from Superblocks to industrial zones-including and detailing all stages of the lockdown. Several comparisons were conducted with the monitoring data available from the former years (2019 and 2018-when available). The results of the analysis in Barcelona show a drastic LAeq reduction (-9 dBA), especially in nightlife areas of the city, moderate to high LAeq change (-7 dBA) in commercial and restaurants areas and a small decrease in LAeq (-5 dBA) in dense traffic areas.

A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments.

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.

Acoustic Description of the Soundscape of a Real-Life Intensive Farm and Its Impact on Animal Welfare: A Preliminary Analysis of Farm Sounds and Bird Vocalisations.

Poultry meat is the world's primary source of animal protein due to low cost and is widely eaten at a global level. However, intensive production is required to supply the demand although it generates stress to animals and welfare problems, which have to be reduced or eradicated for the better health of birds. In this study, bird welfare is measured by certain indicators: CO2, temperature, humidity, weight, deaths, food, and water intake. Additionally, we approach an acoustic analysis of bird vocalisations as a possible metric to add to the aforementioned parameters. For this purpose, an acoustic recording and analysis of an entire production cycle of an intensive broiler Ross 308 poultry farm in the Mediterranean area was performed. The acoustic dataset generated was processed to obtain the Equivalent Level (Leq), the mean Peak Frequency (PF), and the PF variation, every 30 min. This acoustical analysis aims to evaluate the relation between traditional indicators (death, weight, and CO2) as well as acoustical metrics (equivalent level impact (Leq) and Peak Frequency) of a complete intensive production cycle. As a result, relation between CO2 and humidity versus Leq was found, as well as decreases in vocalisation when the intake of food and water was large.

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

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.

Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities.

This Special Issue is focused on all the technologies necessary for the development of an efficient wireless acoustic sensor network, from the first stages of its design to the tests conducted during deployment; its final performance; and possible subsequent implications for authorities in terms of the definition of policies. This Special Issue collects the contributions of several LIFE and H2020 projects aimed at the design and implementation of intelligent acoustic sensor networks, with a focus on the publication of good practices for the design and deployment of intelligent networks in any locations.

Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review.

High Frequency (HF) communications through ionospheric reflection is a widely used technique specifically for maritime, aeronautical, and emergency services communication with remote areas due to economic and management reasons, and also as backup system. Although long distance radio links can be established beyond line-of-sight, the availability, the usable frequencies and the capacity of the channel depends on the state of the ionosphere. The main factors that affect the ionosphere are day-night, season, sunspot number, polar aurora and earth magnetic field. These effects impair the transmitted wave, which suffers attenuation, time and frequency dispersion. In order to increase the knowledge of this channel, the ionosphere has been sounded by means of narrowband and wideband waveforms by the research community all over the world in several research initiatives. This work intends to be a review of remarkable projects for vertical sounding with a world wide network and for oblique sounding for high latitude, mid latitude, and trans-equatorial latitude.

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

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.

An Approach to Frequency Selectivity in an Urban Environment by Means of Multi-Path Acoustic Channel Analysis.

The improvement of quality of life in the framework of the smart city paradigm cannot be limited to a set of objective measures carried out over several critical parameters (e.g., noise or air pollution). Noise disturbances depend not only on the equivalent level LAeq measured, but also on the spectral distribution of the sounds perceived by people. Propagation modelling to conduct auralization can be done either with geometrical acoustics or with wave-based methods, given the fact that urban environments are acoustically complex scenarios. In this work, we present a first analysis of the acoustic spectral distribution of street noise, based on the frequency selectivity of the urban outdoor channel and its corresponding coherence bandwidth. The analysis was conducted in the framework of the data collected in the Milan pilotWASN of the DYNAMAP LIFE project, with the use of three simulated acoustic impulse responses. The results show the clear influence of the evaluated coherence bandwidth of each of the simulated channels over real-life acoustic samples, which leads us to the conclusion that all raw acoustic samples have to be considered as wide-band. The results also depict a dependence of accumulated energy at the receiver with the coherence bandwidth of the channel. We conclude that, the higher the delay spread of the channel, the narrower the coherence bandwidth and the higher the distortion suffered by acoustic signals. Moreover, the accumulated energy of the received signal along the frequency axis tends to differ from the accumulated energy of the transmitted signal when facing narrow coherence bandwidth channels; whereas the accumulated energy along the time axis diverges from the accumulated transmitted energy when facing wide coherence bandwidth channels.

A WASN-Based Suburban Dataset for Anomalous Noise Event Detection on Dynamic Road-Traffic Noise Mapping.

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 L A e q ). 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.

Construction of an Omnidirectional Parametric Loudspeaker Consisting in a Spherical Distribution of Ultrasound Transducers.

Omnidirectional sound sources are needed to perform a large variety of tests in acoustics. Typically, they consist of conventional speaker drivers arranged in a dodecahedron. However, the directivity of the speaker drivers sharpens with frequency, which induces an intense decrease of the sound pressure levels at the edges of the dodechaedron. In this work, the problem is mitigated by building an Omnidirectional Parametric Loudspeaker (OPL), which contains hundreds of small ultrasound transducers set on a sphere. Each transducer emits an ultrasonic carrier wave modulated by an audible signal. Thanks to nonlinear propagation, the air itself demodulates the signal bringing it back to the audible range. The construction of an OPL prototype is challenging. The structure has been built by 3D-printing a set of pieces that conform to the sphere. Each piece contains the exact location of the transducers, which are aligned in parallels to facilitate the structural assembly and the wiring. The performance of the OPL has been tested in an anechoic chamber. Measurements show that the OPL has a good omnidirectional behavior for most frequencies. It clearly improves the directivity of dodechaedral sources in the high frequency range, but performs worse at low frequencies.

Real-Time Distributed Architecture for Remote Acoustic Elderly Monitoring in Residential-Scale Ambient Assisted Living Scenarios.

Ambient Assisted Living (AAL) has become a powerful alternative to improving the life quality of elderly and partially dependent people in their own living environments. In this regard, tele-care and remote surveillance AAL applications have emerged as a hot research topic in this domain. These services aim to infer the patients' status by means of centralized architectures that collect data from a set of sensors deployed in their living environment. However, when the size of the scenario and number of patients to be monitored increase (e.g., residential areas, retirement homes), these systems typically struggle at processing all associated data and providing a reasonable output in real time. The purpose of this paper is to present a fog-inspired distributed architecture to collect, analyze and identify up to nine acoustic events that represent abnormal behavior or dangerous health conditions in large-scale scenarios. Specifically, the proposed platform collects data from a set of wireless acoustic sensors and runs an automatic two-stage audio event classification process to decide whether or not to trigger an alarm. Conducted experiments over a labeled dataset of 7116 s based on the priorities of the Fundació Ave Maria health experts have obtained an overall accuracy of 94.6%.

Detection of Anomalous Noise Events on Low-Capacity Acoustic Nodes for Dynamic Road Traffic Noise Mapping within an Hybrid WASN.

One of the main aspects affecting the quality of life of people living in urban and suburban areas is the continuous exposure to high road traffic noise (RTN) levels. Nowadays, thanks to Wireless Acoustic Sensor Networks (WASN) noise in Smart Cities has started to be automatically mapped. To obtain a reliable picture of the RTN, those anomalous noise events (ANE) unrelated to road traffic (sirens, horns, people, etc.) should be removed from the noise map computation by means of an Anomalous Noise Event Detector (ANED). In Hybrid WASNs, with master-slave architecture, ANED should be implemented in both high-capacity (Hi-Cap) and low-capacity (Lo-Cap) sensors, following the same principle to obtain consistent results. This work presents an ANED version to run in real-time on μ Controller-based Lo-Cap sensors of a hybrid WASN, discriminating RTN from ANE through their Mel-based spectral energy differences. The experiments, considering 9 h and 8 min of real-life acoustic data from both urban and suburban environments, show the feasibility of the proposal both in terms of computational load and in classification accuracy. Specifically, the ANED Lo-Cap requires around 1 6 of the computational load of the ANED Hi-Cap, while classification accuracies are slightly lower (around 10%). However, preliminary analyses show that these results could be improved in around 4% in the future by means of considering optimal frequency selection.

An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments.

One of the main aspects affecting the quality of life of people living in urban and suburban areas is their continued exposure to high Road Traffic Noise (RTN) levels. Until now, noise measurements in cities have been performed by professionals, recording data in certain locations to build a noise map afterwards. However, the deployment of Wireless Acoustic Sensor Networks (WASN) has enabled automatic noise mapping in smart cities. In order to obtain a reliable picture of the RTN levels affecting citizens, Anomalous Noise Events (ANE) unrelated to road traffic should be removed from the noise map computation. To this aim, this paper introduces an Anomalous Noise Event Detector (ANED) designed to differentiate between RTN and ANE in real time within a predefined interval running on the distributed low-cost acoustic sensors of a WASN. The proposed ANED follows a two-class audio event detection and classification approach, instead of multi-class or one-class classification schemes, taking advantage of the collection of representative acoustic data in real-life environments. The experiments conducted within the DYNAMAP project, implemented on ARM-based acoustic sensors, show the feasibility of the proposal both in terms of computational cost and classification performance using standard Mel cepstral coefficients and Gaussian Mixture Models (GMM). The two-class GMM core classifier relatively improves the baseline universal GMM one-class classifier F1 measure by 18.7% and 31.8% for suburban and urban environments, respectively, within the 1-s integration interval. Nevertheless, according to the results, the classification performance of the current ANED implementation still has room for improvement.

An FPGA-Based WASN for Remote Real-Time Monitoring of Endangered Species: A Case Study on the Birdsong Recognition of Botaurus stellaris.

Fast environmental variations due to climate change can cause mass decline or even extinctions of species, having a dramatic impact on the future of biodiversity. During the last decade, different approaches have been proposed to track and monitor endangered species, generally based on costly semi-automatic systems that require human supervision adding limitations in coverage and time. However, the recent emergence of Wireless Acoustic Sensor Networks (WASN) has allowed non-intrusive remote monitoring of endangered species in real time through the automatic identification of the sound they emit. In this work, an FPGA-based WASN centralized architecture is proposed and validated on a simulated operation environment. The feasibility of the architecture is evaluated in a case study designed to detect the threatened Botaurus stellaris among other 19 cohabiting birds species in The Parc Natural dels Aiguamolls de l'Empord.