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Taking aim at New Yorkers’ biggest civic complaint – noise – a team of scientists from NYU, working with collaborators at Ohio State University, have launched a first-of-its-kind comprehensive research initiative to understand and address noise pollution in New York City and beyond. The project – which involves large-scale noise monitoring – leverages the latest in machine learning technology, big data analysis, and citizen science reporting to more effectively monitor, analyze, and mitigate urban noise pollution. Known as Sounds of New York City (SONYC), this multi-year project has received a $4.6 million grant from the National Science Foundation and has the support of City health and environmental agencies.
Our objectives are to create technological solutions for:
Noise pollution is one of the topmost quality of life issues for urban residents in the United States. It has been estimated that 9 out of 10 adults in New York City are exposed to excessive noise levels, i.e. beyond the limit of what the EPA considers to be harmful. When applied to U.S. cities of more than 4 million inhabitants, such estimates extend to over 72 million urban residents. Continued exposure to noise pollution has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease and hearing loss. There is also evidence of impact on educational performance, with studies showing that noise pollution produces learning and cognitive impairment in children, resulting in decreased memory, reading skills and lower test scores.
NYC Health: Effect of Noise and Light on Sleep in New York City - September 2018
NYC Health: Ambient Noise Disruption in New York City - April 2014
NYC DEP: A Guide to New York City’s Noise Code
CENYC: Neighborhood Noise and Its Consequences - 2007
Lancet: Auditory and Non-auditory effects of noise on health
At SONYC we are developing a smart cities, cyber-physical system that includes a distributed network of sensors making use of cutting-edge machine listening methods for large-scale noise reporting, and to constantly provide a rich description of local acoustic environments. To facilitate this we need ground truth data to train machine learning models. Zooniverse volunteers can help us immensely in this task by identifying and labeling the sources of the different sounds found in the examples of urban audio recordings taken from the data our sensors have been gathering over the last two years. In doing so you will join our team of experts in acoustics, machine listening, distributed networking, citizen science, digital media, machine learning, data analysis and visualization, and be contributing to our efforts to better understand the city and its patterns of noise pollution; and most importantly helping to tackle one of the biggest quality of life challenges facing urban citizens.
For more information about the project please go to: SONYC