Environmental noise monitoring using source classification in sensors

Maijala, Panu; Shuyang, Zhao; Heittola, Toni; Virtanen, Tuomas

doi:10.1016/j.apacoust.2017.08.006

Cited by 88 publications

(40 citation statements)

References 11 publications

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“…In [84], an AED approach identifies target sounds from background noise to assign the measured sound levels to the present different sound sources. e AED is based on a binary classifier that discriminates the target sound from the background noise (e.g., traffic, wind, rain, thunder, and birds).…”

Section: Acoustic Event Detection In Urban Sensor Networkmentioning

confidence: 99%

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

Álías

Alsina-Pagès

2019

Journal of Sensors

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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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Section: Acoustic Event Detection In Urban Sensor Networkmentioning

confidence: 99%

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

Álías

Alsina-Pagès

2019

Journal of Sensors

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“…Recent technological developments related to the availability of cheaper and smaller equipment and innovations in communication networks and acoustic signal processing have led to the emergence of low-cost environmental noise sensor networks [18]. In recent years, several projects based on low-cost environmental noise sensor networks have been developed [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Digital Signal Processor Based Acoustic Sensor for Outdoor Noise Monitoring in Smart Cities

López

Alonso

Asensio

et al. 2020

Sensors

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Presently, large cities have significant problems with noise pollution due to human activity. Transportation, economic activities, and leisure activities have an important impact on noise pollution. Acoustic noise monitoring must be done with equipment of high quality. Thus, long-term noise monitoring is a high-cost activity for administrations. For this reason, new alternative technological solutions are being used to reduce the costs of measurement instruments. This article presents a design for a versatile electronic device to measure outdoor noise. This device has been designed according to the technical standards for this type of instrument, which impose strict requirements on both the design and the quality of the device’s measurements. This instrument has been designed under the original equipment manufacturer (OEM) concept, so the microphone–electronics set can be used as a sensor that can be connected to any microprocessor-based device, and therefore can be easily attached to a monitoring network. To validate the instrument’s design, the device has been tested following the regulations of the calibration laboratories for sound level meters (SLM). These tests allowed us to evaluate the behavior of the electronics and the microphone, obtaining different results for these two elements. The results show that the electronics and algorithms implemented fully fit within the requirements of type 1 noise measurement instruments. However, the use of an electret microphone reduces the technical features of the designed instrument, which can only fully fit the requirements of type 2 noise measurement instruments. This situation shows that the microphone is a key element in this kind of instrument and an important element in the overall price. To test the instrument’s quality and show how it can be used for monitoring noise in smart wireless acoustic sensor networks, the designed equipment was connected to a commercial microprocessor board and inserted into the infrastructure of an existing outdoor monitoring network. This allowed us to deploy a low-cost sub-network in the city of Málaga (Spain) to analyze the noise of conflict areas due to high levels of leisure noise. The results obtained with this equipment are also shown. It has been verified that this equipment meets the similar requirements to those obtained for type 2 instruments for measuring outdoor noise. The designed equipment is a two-channel instrument, that simultaneously measures, in real time, 86 sound noise parameters for each channel, such as the equivalent continuous sound level (Leq) (with Z, C, and A frequency weighting), the peak level (with Z, C, and A frequency weighting), the maximum and minimum levels (with Z, C, and A frequency weighting), and the impulse, fast, and slow time weighting; seven percentiles (1%, 5%, 10%, 50%, 90%, 95%, and 99%); as well as continuous equivalent sound pressure levels in the one-third octave and octave frequency bands.

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“…"Traffic noise is an undesirable sound", the noise level, related to urban problems and noise pollution generated by traffic, is one of the major problems of the lack of urban planning, responsible for reducing the quality of life of the population, and to cause degradation of acoustic comfort in urban areas. (Ow and Ghosh, 2017;Maijala et al, 2012;Debnath and Singh, 2018;Morillas et al, 2002, Lee, 2007. In this sense, it can be affirmed that considering the problems caused by the movement of goods and people in urban areas, evaluating the problems and the actions to control the adverse effects are becoming a concern for the community, evidenced by the great number of laws and regulations that limiting noise (Mansouri et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Cai et al (2015) carried out a study on road traffic noise mapping through the creation of a regional noise calculation algorithm for the Guangzhou region of China. Maijala et al (2012) proposed a concept of SPL monitoring by means of an algorithm, which classifies the sound sources by means of a wire sensor.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear regression model for predicting environmental traffic noise

Corsi

Aragão²,

Chiroli³

et al. 2020

Navus

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Transport is an essential activity for urban life and for the economy, however, bad planning of the city script causes some movement in some places, causing excessive noise levels generated by road traffic. We aim to present a mathematical model that represents the relationship between the equivalent level of sound and vehicle volume quantities using the nonlinear logistic regression model in order to facilitate decision-making in relation to urban planning. We collected the data of sound pressure and vehicle volume for ten days during five hours totaling a collection of 50 hours, with a total of 180 thousand data of sound pressure level. The place of data collection is a highway that passes through the city of Maringá, in Paraná, being an intense traffic highway. The results show the performance of the proposed model were differences between measured and predicted traffic noise levels ranged from -0.3 dB (A) to +0.3 dB (A) and the mean difference was -0.006 dB (A). The proposed road traffic noise forecasting model can be used as a decision support tool in urban planning, considering the same boundary conditions as the road study. Keywords: Sound Equivalent Level. Traffic noise. Mathematical Optimization Model. RESUMOMuitos estudos sobre o impacto ambiental em áreas urbanas estão relacionados ao ruído do tráfego rodoviário. O transporte é uma atividade essencial à vida urbana e à economia, seja ele de pessoas ou bens de consumo, os tipos de veículos para transporte são classificados em veículos leves ou pesados. Entretanto, o mau planejamento da roteirização das cidades ocasiona certa movimentação em demasia em alguns pontos, causando níveis excessivos de ruído gerados pelo tráfego rodoviário. Este estudo tem como objetivo apresentar um modelo matemático que represente a relação entre as grandezas Nível Equivalente Sonoro e Volume Veicular, utilizando o modelo de regressão logística não linear, a fim de facilitar a tomada de decisão em relação ao planejamento urbano. Os dados de pressão sonora e volume veicular foram coletados por dez dias durante cinco horas totalizando uma coleta de 50 horas, com um total de 180 mil dados de nível de pressão sonora e 124.316 veículos. O local de coleta de dados foi uma rodovia que passa pela cidade de Maringá, no Paraná, sendo uma rodovia de tráfego intenso. Os resultados mostram que as diferenças entre os níveis de ruído de tráfego medido e previsto variam de -0,3 dB (A) a +0,3 dB (A) e a diferença média foi de -0,006 dB (A). O modelo de previsão de ruído de tráfego rodoviário proposto pode ser utilizado como ferramenta de apoio à decisão no planejamento urbano, levando em consideração as mesmas condições de contorno da rodovia estudada. Palavras-chave: Nível Equivalente Sonoro. Ruído de Tráfego. Modelo de Otimização Matemática.

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Environmental noise monitoring using source classification in sensors

Cited by 88 publications

References 11 publications

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

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

A Digital Signal Processor Based Acoustic Sensor for Outdoor Noise Monitoring in Smart Cities

Nonlinear regression model for predicting environmental traffic noise

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