Abstract
Acoustic pollution has been associated with adverse effects on the health and life expectancy of people, especially when noise exposure happens during the nighttime. With over half of the world population living in urban areas, acoustic pollution is an important concern for city administrators, especially those focused on transportation and leisure noise. Advances in sensor and network technologies made the deployment of Wireless Acoustic Sensor Networks (WASN) possible in cities, which, combined with artificial intelligence (AI), can enable smart services for their citizens. However, the creation of such services often requires structured environmental audio databases to train AI algorithms. This paper reports on an environmental audio dataset of 363 min and 53 s created in a lively area of the Barcelona city center, which targeted traffic and leisure events. This dataset, which is free and publicly available, can provide researchers with real-world acoustic data to help the development and testing of sound monitoring solutions for urban environments.
Highlights
More than four billion people (55% of the world population) live in urban areas, and the projection is that by 2050, this number will increase to seven billion, or two-thirds of the world population [1]
Acoustic pollution has been associated with adverse effects on the health and life expectancy of people, especially when noise exposure happens during the nighttime
With over half of the world population living in urban areas, acoustic pollution is an important concern for city administrators, especially those focused on transportation and leisure noise
Summary
More than four billion people (55% of the world population) live in urban areas, and the projection is that by 2050, this number will increase to seven billion, or two-thirds of the world population [1]. The analysis includes the duration of the events, the signal-to-noise ratio, the number of occurrences, the impact of each occurrence on the background noise LAeq, and the intermittency ratio (IR) of the entire data sample [19,20], which are metrics that have been related to healthy effects in different studies [20] We envision this dataset being used, extended, and combined with others for different purposes, such as the development of noise identification and monitoring solutions, the creation of guidelines for designing sustainable urban and suburban soundscapes, and the comparison with other datasets for health impact studies
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