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Environment Classification for Lrban Big Data Using Deep Learning
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DOI：10.1109/MCOM.2018.1700577.png)
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Abstract
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With the increased population in urban areas, it has become a challenge to a service provider to manage big data coming from various users and devices. One of the fastest growing devices used by the citizens of urban areas is the smart-phone. Today we can find almost nobody with-out at least one smartphone in their possession. While talking through a smartphone, surrounding environmental noises are added to the speech of the talker. Sometimes these noises are very annoying to the listeners. In this article, we propose a communication framework for urban mobile big data integrating an urban environment classification system. The proposed system utilizes a deep learning approach to classify environmental noises so that an appropriate noise cancellation algorithm can be used to reduce the effect of noise while having a conversation through smartphones. The proposed framework uses mobile edge computing technology to provide low-latency and efficient transmission. Experimental results demonstrate that the proposed system is very efficient in classifying environmental noises.
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