Noise monitoring using mobile phones
Date of Issue2014
School of Computer Engineering
Centre for Multimedia and Network Technology
Nowadays, almost every person in the world owns a smartphone. A person would carry his smartphone wherever he goes. This gives a chance to collect some information from the location using some of the sensors embedded in the smartphone. The information that this project is focused on gathering is sound. However, more often than not the sounds will be surrounded by noises. Noise can be defined as unwanted signal. Too much noise can incur a bad effect on health and environment. According to WHO, there are seven documented categories of adverse health effects on humans. The most common effect is hearing impairment. This is caused when a person is exposed to loud noises, especially the ones with Sound Pressure Level (loudness) over 70 dB. Mapping the noise information to a real world map can be the cure. Firstly, it can give government an ability to reduce noise pollution in a city / a country because it provides information of areas which are polluted by noise so that the government can analyse the areas more thoroughly and able to take actions to reduce the noise level. Secondly, it will also be useful for people who are interested in researching a particular area for occurrences of noise as well as the sources of these noises. This project aims to facilitate noise mapping with the help of Android device. The device will be used to collect information on an environment through sound recording. Relevant information, such as relative loudness and sound source, will then be retrieved from the recording by applying digital signal processing technique. Users can also contribute by giving some inputs to the application regarding the sound source. All information will be integrated to Google Map based on the location where the recording is taken. At the end of this project, this application is believed to have met its purpose and can be used as the basis to develop a more sophisticated application to monitor noise in an environment. Adoption of Machine Learning or Data Mining technique can further improve the usefulness of the application as a sound classifier.
DRNTU::Engineering::Computer science and engineering
Final Year Project (FYP)
Nanyang Technological University