Design and Implementation of Audio Detection System

High spatial resolution environmental noise assessment and its associations with risks of cardiovascular diseases based on digital healthcare data in China.

Environmental noise, as well as being a form of environmental pollution that affects mainly urban areas, it is a problem that involves people's quality of life. This paper presents a methodology that takes advantage of the Volunteered Geographic Information (VGI) in obtaining georeferenced environmental noise maps and the corresponding statistics for a particular area of interest. The methodology herein presented considers the phases of Data acquisition, Analysis and data processing, and Visualization of the information. The design and development of a Geographic Information System is presented, which consists of a web mapping application, an application for mobile devices, called NoiseMonitor, Geospatial Analysis and Machine Learning methods (Support Vector Machines and Artificial Neural Networks) for acoustic noise prediction using contextual information, that is, some factors around the measurements. A study case is based on Mexico City.

Governments and commissions all around the globe have worked on developing policies and rules as well as actions and measures for continuous assessment and remedies for environmental noise pollution. All of which requires continuous and accurate measurements of noise levels. The advent of smart city and IoT-based monitoring makes it possible for different technologies to cooperate in collecting and reporting environmental noise levels for longer duration and wider geographical region. Among others, mobile crowdsourcing (MCS) appears to be a promising technique for environmental noise monitoring with minimal upfront cost and almost no recurrent cost for regulators. This is due to the assumption of voluntary participation from mobile device owners who may be reluctant to participate if the recurrent cost of mobile resource consumption is high. In this work, an approach to improve mobile device resources efficiency for mobile crowdsourcing application in environmental noise monitoring is proposed. That is, noise samples from a device are collected only if it is significant to the accuracy of the measurements. Also this paper defines and mathematically formalizes the problem under study and develops two algorithms to optimize both resource and measurements efficiency. The results demonstrate the efficiency of the proposed solution.

There are many reports on the noise pollution and its hazards from many cities around the world. The aims of these investigations were the environmental noise pollution in the populated cities in the world. This issue is not looked at as a severe problem and has not been paid much attention to in Iran. However, we intended to study noise pollution in the urban city of Tabriz ‐ one of the crowded and prominent cities of Iran. We carried out our research at 1000 stations over the city and found that sound level was lower than 65 dB in 7.7% of stations, over 75 dB in 59.8% of stations and 37.5% in the range of 65-75 dB (in particular in the service zones and city centre) during the day.

Background: Epidemiological studies indicate that both traffic noise and outdoor air pollution may be associated with risk for cardiovascular disease. Only few studies consider these two environmental exposures together. Aims: To provide an overview of the latest knowledge on the association between environmental noise and outdoor air pollution on related cardiovascular health effects. To examine whether the evidence suggests that noise and air pollution interact or confound with each other. Methods: A search was conducted on Pubmed electronic database, retrieving the papers on the cardiovascular health effects of combined exposure to noise and outdoor air pollution. The search was limited to peer-reviewed papers published in English between 2005 and February 2013. Results: Few studies have considered the joint effect of traffic noise and air pollution on the cardiovascular system. 13 papers on cardiovascular efects of combined exposure to noise and outdoor air pollution were identified and reviewed. Road traffic noise was the most commonly investigated noise source. The main reviewed cardiovascular health outcomes included: arterial blood pressure and hypertension, coronary heart disease, myocardial infarction and stroke. The effects of additional adjustment for either noise or air pollution varied between studies. Conclusions: Both traffic noise and outdoor air pollution seemed to have an independent effects on cardiovascular health end points. There is a need of more well-designed longitudinal studies to further investigate the combined exposure from noise and outdoor air pollution.

Environmental noise pollution is a significant concern for public health and well-being. It is important to accurately identify and classify environmental noise sources to develop effective noise reduction strategies. This paper examines using an audio spectrogram transformer (AST) for environmental noise tagging tasks. The AST is a pure attention-based model that takes the spectrograms of the audio signals as input and calculates the self-attention without convolutions. Previously, it was pre-trained on large datasets such as ImageNet and AudioSet, showing higher precision than prior work. Although the hyperparameters were given, many have not been clear from the previous literature. Results show that there are a few choices for the patch split overlap, more overlap does not result in significantly improved performance. It is also shown that instead of the default 128 frequency bins, 96 is another choice, which can reduce the computations. The results further show that 30% - 40% can be masked for the frequency, 20% - 50% for the time dimension. The trained model is further tested on the dataset of different environmental noise sources collected by SiteHive Hexanodes across Australia and New Zealand. Results show that the AST model can achieve high accuracy in identifying different environmental noises.

With the advent of reliable and continuously operating noise monitoring systems, we are now faced with an unprecedented amount of noise monitor data. In the context of environmental noise monitoring, there is a need to automatically detect, separate, and classify all environmental noise sources. This is a complex task because sources can overlap, vary by location, and have an unbounded number of noise sources that a monitor device may record. In this study, we synthetically generate datasets that contain Gaussian noise and overlaps for several pre-labeled environmental noise monitoring datasets to examine how well deep learning methods (e.g., autoencoders) can separate environmental noise sources. In addition to examining performance, we also focus on understanding which signal features and separation metrics are useful to this problem.

The impact of environmental noise, poor biodiversity, and air pollution on urban populations poses a significant challenge to public health. Addressing these concerns requires a comprehensive approach that considers the perceptual experiences of citizens and objective environmental data. The Conscious Walks introduce a hybrid methodology designed to address the perceptive impact of citizens on their environment. Conscious Walks are citizen-science participative projects that combine the active involvement of citizens with scientific data collection. Implemented and tested in Barcelona, Conscious Walks provide a unique opportunity to assess urban soundscapes, and this contribution specifically evaluates the soundscapes of the Ciutadella Park in Barcelona for three consecutive years. The gathered data is carefully cross-referenced with information on air quality and biodiversity obtained during the walks. The novelty of this research lies in the development of integrated indicators that comprehensively capture the interplay between biodiversity, noise pollution, and air pollution. These indicators provide an understanding of urban environmental quality, offering valuable insights for sustainable urban planning and policy-making. The results presented in this contribution underscore the significance of adopting holistic methodologies to address the complex challenges posed by urban environments, emphasizing the need for integrated indicators in assessing the multifaceted aspects of urban sustainability.

This work presents an approach based on the concept of Volunteered Geographic Information (VGI) to monitoring environmental noise; it is a problem that specially affects people's quality of life in urban areas. In this work, mobile devices are used to massively collect environmental noise measurements, which are used to generate maps of a specific area and then make a forecast. The difference of this approach with respect to the traditional methods based on monitoring stations is that it is much less costly, which is a major innovation in developing countries. This approach is based on a GIS approach that consists of an application for mobile devices and a web mapping application; including Geospatial Analysis and Machine Learning methods for the acoustic noise prediction using contextual information. With this approach it will be possible to define actions to mitigate the effects of environmental noise, it is aligned in the context of the smart cities. The proposed case study is based on collecting noise data of the Mexico City.

Urbanization and industrialization present significant challenges to public health, particularly in managing environmental noise pollution. This study addresses the critical issue of noise pollution at the study area by developing a comprehensive noise map that encompasses industrial zones, construction sites, and areas with heavy traffic. Utilizing ArcGIS modeling software, the research visualizes the spatial dynamics of noise pollution, resulting in a detailed map that represents noise levels throughout the area. The analysis reveals that noise levels during weekday peak hours average 70.5 dBA, markedly higher than the 65.5 dBA average recorded during weekend peak hours. During weekdays morning peak, noise levels indicate three monitoring points in the red zone, two in the yellow zone, and five in the green zone. Notably, noise levels at all ten monitoring locations during weekdays peak hours exceeded the maximum permissible sound level of 60 dBA set by the Department of Environment for residential and commercial areas. These findings contribute to the development of targeted noise mitigation strategies and serve as a valuable resource for environmental authorities, policymakers, and urban planners. This research underscores the importance of a comprehensive approach to addressing environmental noise pollution and provides a practical framework for urban areas facing similar challenges.

As cities in sub-Saharan Africa become more crowded, noise pollution is also emerging as an important environmental concern, after air pollution. Yet, unlike air pollution, which is enjoying relatively more public attention, there is limited measurement data and policy efforts on environmental noise pollution. We followed a recent city-wide measurement approach used in Accra (Ghana) and characterized environmental noise patterns in Kigali, a contrasting city with very different topography and regulatory system than Accra to inform urban policy. We established 10 ‘fixed’ (yearlong) and 120 ‘rotating’ (weeklong) monitoring sites to capture both the temporal and spatial patterns in Kigali’s sound environment. The measurement occurred between November 2022 and December 2023, and samples were collected at 1 min interval, resulting in 5155 014 (3580 site-days) and 1190 620 (827 site-days) site-minutes of valid data from the fixed and rotating sites, respectively. The 130 monitoring sites covered a variety of geographic and land-use factors across diverse neighborhoods and sources. We computed several noise metrics, including 1 h (LAeq1 h), daily (LAeq24 h), day-time (Lday), and night-time (Lnight). Daily noise (LAeq24 h) levels across the city ranged between 38 dBA and 85 dBA. Commercial, business, and industrial (CBI) and high-density residential (HD) communities experienced the highest noise levels, with some sites constantly above 70 dBA at day and 65 dBA at night. About 63% of our observed day-time values (up to ~72% in some areas) exceeded the Rwandan day-time standard (55 dBA) for residential areas, whereas 69% of the observed night-time values (up to 80% in some areas) exceeded the corresponding night-time standard (45 dBA). In Nyarugenge, the most urbanized district, as much as 75% of our site-days data exceeded day-time standard. However diurnal patterns throughout the city were similar, rising from ~5 am, peaking at about 8 am and plateauing until 6 pm before falling to their lowest at midnight. Overall, noise levels in the city did not vary much by day of the week, weekdays vs weekend, or dry vs wet seasons. Environmental noise in Kigali often exceeded both Rwandan standards and international guidelines, with residents in the city center district, CBI and HD areas at risk of higher exposure, and hence higher risk of adverse effects. Detailed assessment of the sources, at-risk population, and associated health effects may inform Rwandan’s environmental policy efforts and city initiatives in the face of the ongoing urban growth and densification.

Full data coverage of urban environments is crucial to monitor the status of the area to detect, for instance, trends and detrimental environmental changes. Collecting observations related to environmental factors such as noise pollution in urban environments through classical approaches implies the deployment of Sensor Networks. The cost of deployment and maintenance of such infrastructure might be relatively high for local and regional governments. On the other hand recent mass-market mobile devices such as smartphones are full of sensors. For instance, it is possible to perform measurements of noise through its microphone. Therefore they become low-cost measuring devices that many citizens have in their pocket. In this paper we present an approach for gathering noise pollution data by using mobile applications. The applications are designed following gamification techniques to encourage users to participate using their personal smartphones. In this way the users are involved in taking and sharing noise pollution measurements in their cities that other stakeholders can use in their analysis and decision making processes.

SMARTer Growth (SG) Neighbourhood Design principles have evolved out of extensive research on exploring ways to make development patterns and housing forms into more livable and sustainable communities. SG has been investigated in several studies that report on its key features; however, no study has been able to draw on any systematic research into evaluating the impact of the full-fledged SG principles on community quality of life (QoL). Numerous studies suggest that measuring the influence of neighbourhood design on QoL is exceedingly complex. In addition, the wide variation of the proposed QoL indicators reflects how there is no global agreement on the definition of QoL nor the importance of the factors that influence it. Therefore, this paper will not attempt to develop the perfect neighborhood system design that ‘maximizes’ human QoL. Rather, it aims to identify a reliable suite of empirical tools that could be utilized by engineers to objectively evaluate key factors of a neighbourhood’s design using previously researched QoL metrics. In particular, six tools are utilized in this research, to evaluate the QoL outcomes of hypothetically retrofitting neighbourhoods in the city of Kelowna using the SG principles, including i-Thrive, air quality, noise pollution, walkability, bikeability, and playability. The results show that applying the SG principles in the three urban centres resulted in an increase in the i-THRIVe score, walkability, bikeability, and playability and a decrease in noise pollution. However, the SG scenario resulted in an increase in traffic air pollution compared to the business as usual scenario.

Although noise pollution is critical, its intangibility—lacking visibility, taste, or smell—often leads to it receiving less attention compared to other pollution types like air or water pollution. While the air around us is constantly filled with sounds, we may not always perceive it as noise. As researchers and policymakers increasingly emphasize aspects of quality of life, urban visual pollution has garnered growing attention, particularly in developed countries. Accordingly, the study focuses on noise and visual pollution to give an overview about awareness and current situation in one of the developing countries. Noise and visual pollution significantly impact public health. Noise pollution stemming from sources such as construction, motorized vehicles, and aircraft triggers anxiety, stress, and sleep disturbances. Prolonged exposure is associated with cardiovascular disease. Visual pollution, encompassing unsightly elements in the environment, impairs balance and heightens biological stress responses. Addressing these issues necessitates community awareness and the implementation of effective mitigation strategies based on community perception. The study provides an inclusive overview of noise environmental awareness and visual environmental awareness. Conducted through online questionnaires and result analysis, it aims to comparatively analyze these aspects and formulate targeted mitigation strategies.

For public health policy and planning it is important to understand the relative contribution of environmental noise on health compared to other environmental stressors. Air pollution is the primary environmental stressor in relation to cardiovascular morbidity and mortality. This paper reports a narrative review of studies in which the associations of both environmental noise and air pollution with health have been examined. Studies of hypertension, myocardial infarction, stroke, mortality and cognitive outcomes were included. Results suggest independent effects of environmental noise from road traffic, aircraft and, with fewer studies, railway noise on cardiovascular outcomes after adjustment for air pollution. Comparative burden of disease studies demonstrate that air pollution is the primary environmental cause of disability adjusted life years lost (DALYs). Environmental noise is ranked second in terms of DALYs in Europe and the DALYs attributed to noise were more than those attributed to lead, ozone and dioxins. In conclusion, in planning and health impact assessment environmental noise should be considered an independent contributor to health risk which has a separate and substantial role in ill-health separate to that of air pollution.