A Feasibility Study on TinyML-Based Framework for Categorical Urban Noise Detection Using Low-Cost Sensors: A Systematic Review

Abstract

A rise in Urbanisation has vastly increased the number of environmental issues related to urban living, including, most significantly, Noise Pollution, which is now seen as a major Public Health Threat to the residents of contemporary urban centres. Numerous studies have shown that rapid urbanisation can contribute significantly to Mental Health Issues caused by individuals living in highly dense environments with sensory overload (Trivedi et al. 2008), whereby long-term exposure to high-intensity urban soundscapes is not simply a nuisance; but rather, has now become a major health risk for individuals leading to increases in Sleep Disorders, Impaired Cognitive Function and Cardiovascular Disease (Clark and Paunovic 2018). Therefore, to address these and related urban issues, accurate Noise Mapping and Continuous Environmental Monitoring are now critical to Modern Health Management and Urban Planning.