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.

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.

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.

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.

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.

homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring.

The consistent growth in human life expectancy during the recent years has driven governments and private organizations to increase the efforts in caring for the eldest segment of the population. These institutions have built hospitals and retirement homes that have been rapidly overfilled, making their associated maintenance and operating costs prohibitive. The latest advances in technology and communications envisage new ways to monitor those people with special needs at their own home, increasing their quality of life in a cost-affordable way. The purpose of this paper is to present an Ambient Assisted Living (AAL) platform able to analyze, identify, and detect specific acoustic events happening in daily life environments, which enables the medic staff to remotely track the status of every patient in real-time. Additionally, this tele-care proposal is validated through a proof-of-concept experiment that takes benefit of the capabilities of the NVIDIA Graphical Processing Unit running on a Jetson TK1 board to locally detect acoustic events. Conducted experiments demonstrate the feasibility of this approach by reaching an overall accuracy of 82% when identifying a set of 14 indoor environment events related to the domestic surveillance and patients' behaviour monitoring field. Obtained results encourage practitioners to keep working in this direction, and enable health care providers to remotely track the status of their patients in real-time with non-invasive methods.

Influence of lips on the production of vowels based on finite element simulations and experiments.

Three-dimensional (3-D) numerical approaches for voice production are currently being investigated and developed. Radiation losses produced when sound waves emanate from the mouth aperture are one of the key aspects to be modeled. When doing so, the lips are usually removed from the vocal tract geometry in order to impose a radiation impedance on a closed cross-section, which speeds up the numerical simulations compared to free-field radiation solutions. However, lips may play a significant role. In this work, the lips' effects on vowel sounds are investigated by using 3-D vocal tract geometries generated from magnetic resonance imaging. To this aim, two configurations for the vocal tract exit are considered: with lips and without lips. The acoustic behavior of each is analyzed and compared by means of time-domain finite element simulations that allow free-field wave propagation and experiments performed using 3-D-printed mechanical replicas. The results show that the lips should be included in order to correctly model vocal tract acoustics not only at high frequencies, as commonly accepted, but also in the low frequency range below 4 kHz, where plane wave propagation occurs.

Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring.

One of the main priorities of smart cities is improving the quality of life of their inhabitants. Traffic noise is one of the pollutant sources that causes a negative impact on the quality of life of citizens, which is gaining attention among authorities. The European Commission has promoted the Environmental Noise Directive 2002/49/EC (END) to inform citizens and to prevent the harmful effects of noise exposure. The measure of acoustic levels using noise maps is a strategic issue in the END action plan. Noise maps are typically calculated by computing the average noise during one year and updated every five years. Hence, the implementation of dynamic noise mapping systems could lead to short-term plan actions, besides helping to better understand the evolution of noise levels along time. Recently, some projects have started the monitoring of noise levels in urban areas by means of acoustic sensor networks settled in strategic locations across the city, while others have taken advantage of collaborative citizen sensing mobile applications. In this paper, we describe the design of an acoustic low-cost sensor network installed on public buses to measure the traffic noise in the city in real time. Moreover, the challenges that a ubiquitous bus acoustic measurement system entails are enumerated and discussed. Specifically, the analysis takes into account the feature extraction of the audio signal, the identification and separation of the road traffic noise from urban traffic noise, the hardware platform to measure and process the acoustic signal, the connectivity between the several nodes of the acoustic sensor network to store the data and, finally, the noise maps' generation process. The implementation and evaluation of the proposal in a real-life scenario is left for future work.

Narrow-band autocorrelation function features for the automatic recognition of acoustic environments.

Acoustic environments are typically composed of multiple sound sources of different typologies, making them especially complex to model and parameterize. To develop an automatic acoustic environment recognition system, this work proposes a spectro-temporal signal parameterization technique inspired by human perception. The proposed parameters are derived from the analysis of the autocorrelation function of narrow-band signals (NB-ACF) obtained from an auditory gammatone filter bank. Five features related to acoustic phenomena are extracted from the NB-ACF to parameterize sound signals. Experiments are conducted on a 4 h sound database (composed of 15 different acoustic environments) employing four different machine learning techniques: K-nearest neighbor, Gaussian mixture models, neural networks and support vector machines. The averaged recognition rates increase by 4.5% when using the proposed NB-ACF features instead of the popular Mel frequency cepstral coefficients. The improvement is even greater (5.6%) when the features are tested in acoustic environments from unknown locations. These results are attributed to the better modeling of the acoustic environments thanks to the complementarity of the spectro-temporal features derived from NB-ACF analysis.

Effects of head geometry simplifications on acoustic radiation of vowel sounds based on time-domain finite-element simulations.

One of the key effects to model in voice production is that of acoustic radiation of sound waves emanating from the mouth. The use of three-dimensional numerical simulations allows to naturally account for it, as well as to consider all geometrical head details, by extending the computational domain out of the vocal tract. Despite this advantage, many approximations to the head geometry are often performed for simplicity and impedance load models are still used as well to reduce the computational cost. In this work, the impact of some of these simplifications on radiation effects is examined for vowel production in the frequency range 0-10 kHz, by means of comparison with radiation from a realistic head. As a result, recommendations are given on their validity depending on whether high frequency energy (above 5 kHz) should be taken into account or not.

On the Impact of Anomalous Noise Events on Road Traffic Noise Mapping in Urban and Suburban Environments.

Noise pollution is a critical factor affecting public health, the relationship between road traffic noise (RTN) and several diseases in urban areas being especially disturbing. The Environmental Noise Directive 2002/49/EC and the CNOSSOS-EU framework are the main instruments of the European Union to identify and combat noise pollution, requiring Member States to compose and publish noise maps and noise management action plans every five years. Nowadays, the noise maps are starting to be tailored by means of Wireless Acoustic Sensor Networks (WASN). In order to exclusively monitor the impact of RTN on the well-being of citizens through WASN-based approaches, those noise sources unrelated to RTN denoted as Anomalous Noise Events (ANEs) should be removed from the noise map generation. This paper introduces an analysis methodology considering both Signal-to-Noise Ratio (SNR) and duration of ANEs to evaluate their impact on the A-weighted equivalent RTN level calculation for different integration times. The experiments conducted on 9 h of real-life data from the WASN-based DYNAMAP project show that both individual high-impact events and aggregated medium-impact events bias significantly the equivalent noise levels of the RTN map, making any derived study about public health impact inaccurate.