Investigation on Machine Learning Approaches for Environmental Noise Classifications

ALBAJI, ALI OTHMAN; Rashid, Rozeha A.; Hamid, S. Z. Abdul

doi:10.1155/2023/3615137

Cited by 3 publications

(1 citation statement)

References 23 publications

(29 reference statements)

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“…In [27], Bhoi et al present a sismilar study, performed in India, In ref. [25], Albaji et al, investigate the use of ML models to classify environmental sounds that are considered noise pollution in smart cities. Different types of noise were collected in sixteen cities in Malaysia.…”

Section: Search Results and Analysismentioning

confidence: 99%

Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data

Elvas,

Nunes,

Ferreira

et al. 2023

Applied Sciences

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Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development.

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Section: Search Results and Analysismentioning

confidence: 99%

Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data

Elvas,

Nunes,

Ferreira

et al. 2023

Applied Sciences

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A systematic review of current AI techniques used in the context of the SDGs

Greif,

Röckel,

Kimmig

et al. 2024

Int J Environ Res

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This study aims to explore the application of artificial intelligence (AI) in the resolution of sustainability challenges, with a specific focus on environmental studies. Given the rapidly evolving nature of this field, there is an urgent need for more frequent and dynamic reviews to keep pace with the innovative applications of AI. Through a systematic analysis of 191 research articles, we classified AI techniques applied in the field of sustainability. Our review found that 65% of the studies applied supervised learning methods, 18% employed unsupervised learning, and 17% utilized reinforcement learning approaches. The review highlights that artificial neural networks (ANN), are the most commonly applied AI techniques in sustainability contexts, accounting for 23% of the reviewed methods. This comprehensive overview of AI techniques identifies key trends and proposes new research avenues to address the complex issue of achieving the Sustainable Development Goals (SDGs). Graphic abstract

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Development of Innovative Algorithm for Sound Detection Based on FFT and Goertzel Algorithms

Umaeswari,

Jyothi

et al. 2024

2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)

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Investigation on Machine Learning Approaches for Environmental Noise Classifications

Cited by 3 publications

References 23 publications

Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data

Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data

A systematic review of current AI techniques used in the context of the SDGs

Development of Innovative Algorithm for Sound Detection Based on FFT and Goertzel Algorithms

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