Artificial intelligence to measure citizen perception towards events of noise pollution

phys.org | 3/19/2019 | Staff
JimmyJoe (Posted by) Level 3
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Researchers from Universidad Politécnica de Madrid have developed an artificial intelligence system to detect and assess noisy activities from social network data.

They developed a system of text analysis that, applied to comments published in social media, is able to automatically detect complaints on noise pollution and classify them according to their origin. This system combines artificial intelligence (machine learning) with diverse techniques of language analysis.

Addition - System - Onset - Events - City

In addition, the system can predict the onset of noisy events, which can help city managers to design early interventions to avoid disturbances and health issues for citizens. The study has been developed in collaboration with Télécom Paristech.

In Europe, it is estimated that 25 percent of the population is exposed to high noise levels, which can increase health risks. This causes public health issues and reduces quality of life, especially in urban areas associated with the lack of rest and stress..

Surveys - Perceptions - Environments - Drawbacks - Cost

Traditional surveys have been used to determine citizen perceptions of noisy urban environments, but these have the drawbacks of high cost derived from development and execution, a limited number of participants, and the duration of the surveying campaign.

Additionally, this system is not agile when detecting problems or specific noisy events. In recent years, new systems of online citizen participation have come up and they allow interaction with local managers, but they are not generally used by the population.

Users - Media - Post - Opinions - Feelings

Users of social media post opinions and feelings about diverse topics: politics, TV, products, and of course, the environment, including noise pollution.

Luis Gascó, a researcher from the group on Instrumentation and Applied Acoustics (I2A2) at UPM...
(Excerpt) Read more at: phys.org
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