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Sustainable Soundscapes for Android – Latest Version & Features
by Alex Zheng
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> 2.2k
Monthly Downloads
~$900
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About Sustainable Soundscapes
It has been known for relatively long periods of time that human-caused noise pollution has detrimental effects on the environment, but exactly how much damage is occurring and how can we prevent it?
This project aims to both measure quantitatively the ecological impact of anthropogenic noise, to determine the level of risk it presents to wildlife in the area, and to further use Regulation Technology (RegTech) to provide remedial solutions through an online token trading interface.
By aggregating previously collected data on levels of noise harmful to specific species (done through literature review) with maps of a regions wildlife and location/sound measurements collected in real-time, a coded application can use tracked sound and location data combined with convex hull and shortest distance algorithms and the inverse square law for audio production to calculate effective noise level at a distance and decide whether or not that level of sound is potentially dangerous.
This data is then tokenized in a credit trading system, which compensates or punishes users/organizations/companies depending on their difference from the calculated threshold.
For example, organizations may be required to pay large fines or consume previously earned tokens in return for potentially damaging the ecosystem, while others may auction tokens earned from successful noise reduction.
Furthermore, repeated randomized simulations prove the applications effectiveness of 95% at only a 50% total usage rate, meeting the original goal with high efficiency, specificity, and accessibility.
To the researcher’s knowledge, there is no other application that can currently perform the same tasks.Privacy Policy: https://sustainablesoundscapes.wordpress.com/2023/08/09/privacy-policy/
This project aims to both measure quantitatively the ecological impact of anthropogenic noise, to determine the level of risk it presents to wildlife in the area, and to further use Regulation Technology (RegTech) to provide remedial solutions through an online token trading interface.
By aggregating previously collected data on levels of noise harmful to specific species (done through literature review) with maps of a regions wildlife and location/sound measurements collected in real-time, a coded application can use tracked sound and location data combined with convex hull and shortest distance algorithms and the inverse square law for audio production to calculate effective noise level at a distance and decide whether or not that level of sound is potentially dangerous.
This data is then tokenized in a credit trading system, which compensates or punishes users/organizations/companies depending on their difference from the calculated threshold.
For example, organizations may be required to pay large fines or consume previously earned tokens in return for potentially damaging the ecosystem, while others may auction tokens earned from successful noise reduction.
Furthermore, repeated randomized simulations prove the applications effectiveness of 95% at only a 50% total usage rate, meeting the original goal with high efficiency, specificity, and accessibility.
To the researcher’s knowledge, there is no other application that can currently perform the same tasks.Privacy Policy: https://sustainablesoundscapes.wordpress.com/2023/08/09/privacy-policy/