Stress-causing noise could now be monitored in urban areas using a new app developed for smartphones by Australian scientists.
Researchers from CSIRO and the University of New South Wales, built the program called Ear-Phone, using existing smartphone technology, to provide more detailed noise profile maps of cities, and help identify noise hotspots.
Noise pollution has been linked to stress in modern day life, with health effects including sleep disruption, hearing impairment, and psychological damage, impacting negatively on productivity
"This information is very important for town planning - if you want to build a school, and for real estate markets [and] buyers choosing the area they want to live in," says the study's lead author Dr Rajib Rana from CSIRO's Autonomous Systems Laboratory.
Rana says Australia does not actually measure ambient noise levels but in the United Kingdom noise maps are produced every five years.
However, he says, these don't measure noise levels directly but are based on models of traffic densities and the noise reflectivity off buildings. They also need to consider changes in levels throughout the day and night, and over small distances.
Rana says such existing methods of determining the noise distribution around a city is a very expensive process.
"Just think of the geographical extent of a city and covering every street," he says.
A better way
"We thought we could use samples from mobile phones as a crowd sourcing method of determining noise levels," says Rana, adding the modern smartphone already has sensors capable of collecting the data required.
"Just think of the built-in sensors [already] fitted to smartphones, such as accelerometers, gyroscopes, proximity sensors, light intensity monitors. Some even have temperature and humidity sensors," says Rana.
Smartphones also provide a location and time stamp to show exactly where and when the measurement was taken.
Rana and colleagues needed to develop an algorithm that records only useful measurements of ambient noise, and not other sounds like conversations, or keys jangling in bags, which could distort readings.
Initial tests were carried out on the streets of Brisbane, and readings were confirmed using commercially available sound meters.
Best results
The authors found the best results were obtained when people were outdoors, with the phone in their hand. Readings were far less accurate inside vehicles, or when the phones were in a bag or case.
"We only want to sample when the phone's in your hand, so the program determines if it's in a bag, pocket or case, by using its light monitor, accelerometer and proximity sensors, to determine its position and unwanted data can then be discarded," says Rana.
"GPS readings are used to determine movement below 6 kilometres per hour, which is the average the speed of the person walking, and a valid GPS signal means they're outdoors."
The research reported on the pre press website ArXiv.org, indicates the program can detect the phone's position with an accuracy of 84 per cent.
The noise level and GPS co-ordinates are then sampled every minute and stored along with the location and time, before being uploaded to the database when a free Wi-Fi connection is detected.
According to Rana, the next challenge is to develop a sustainable incentive mechanism to get people motivated to participate.
Researchers from CSIRO and the University of New South Wales, built the program called Ear-Phone, using existing smartphone technology, to provide more detailed noise profile maps of cities, and help identify noise hotspots.
Noise pollution has been linked to stress in modern day life, with health effects including sleep disruption, hearing impairment, and psychological damage, impacting negatively on productivity
"This information is very important for town planning - if you want to build a school, and for real estate markets [and] buyers choosing the area they want to live in," says the study's lead author Dr Rajib Rana from CSIRO's Autonomous Systems Laboratory.
Rana says Australia does not actually measure ambient noise levels but in the United Kingdom noise maps are produced every five years.
However, he says, these don't measure noise levels directly but are based on models of traffic densities and the noise reflectivity off buildings. They also need to consider changes in levels throughout the day and night, and over small distances.
Rana says such existing methods of determining the noise distribution around a city is a very expensive process.
"Just think of the geographical extent of a city and covering every street," he says.
A better way
"We thought we could use samples from mobile phones as a crowd sourcing method of determining noise levels," says Rana, adding the modern smartphone already has sensors capable of collecting the data required.
"Just think of the built-in sensors [already] fitted to smartphones, such as accelerometers, gyroscopes, proximity sensors, light intensity monitors. Some even have temperature and humidity sensors," says Rana.
Smartphones also provide a location and time stamp to show exactly where and when the measurement was taken.
Rana and colleagues needed to develop an algorithm that records only useful measurements of ambient noise, and not other sounds like conversations, or keys jangling in bags, which could distort readings.
Initial tests were carried out on the streets of Brisbane, and readings were confirmed using commercially available sound meters.
Best results
The authors found the best results were obtained when people were outdoors, with the phone in their hand. Readings were far less accurate inside vehicles, or when the phones were in a bag or case.
"We only want to sample when the phone's in your hand, so the program determines if it's in a bag, pocket or case, by using its light monitor, accelerometer and proximity sensors, to determine its position and unwanted data can then be discarded," says Rana.
"GPS readings are used to determine movement below 6 kilometres per hour, which is the average the speed of the person walking, and a valid GPS signal means they're outdoors."
The research reported on the pre press website ArXiv.org, indicates the program can detect the phone's position with an accuracy of 84 per cent.
The noise level and GPS co-ordinates are then sampled every minute and stored along with the location and time, before being uploaded to the database when a free Wi-Fi connection is detected.
According to Rana, the next challenge is to develop a sustainable incentive mechanism to get people motivated to participate.
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