TY - GEN
T1 - Tefnut
T2 - 11th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2016
AU - Guo, Hansong
AU - Huang, He
AU - Wang, Jianxin
AU - Tang, Shaojie
AU - Zhao, Zhenhua
AU - Sun, Zehao
AU - Sun, Yu E.
AU - Huang, Liusheng
AU - Liu, Hengchang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Real-time and fine-grained rain information is crucial not only for climate research, weather prediction, water resources management, agricultural production, urban planning and natural disasters monitoring, but also for applications in our daily lives. However, because of the lack of rain detection systems and the high variable attribute of rain, both in time and space, the rain detection today is still not precise enough. In such context, we propose and implement Tefnut (Tefnut is the rain deity in Ancient Egyptian religion.), a novel system that exploits opportunistically crowdsourced in-vehicle audio clips from an alternative, nowadays omnipresent source, smartphones, to achieve precise detection of rain leveraging a supervised recognizer constructed from a series of refined features. We conduct extensive experiments, and evaluation results demonstrate that Tefnut can detect the rain with 96.0% true positive rate, when deciding with a one-second-long in-vehicle audio segment only.
AB - Real-time and fine-grained rain information is crucial not only for climate research, weather prediction, water resources management, agricultural production, urban planning and natural disasters monitoring, but also for applications in our daily lives. However, because of the lack of rain detection systems and the high variable attribute of rain, both in time and space, the rain detection today is still not precise enough. In such context, we propose and implement Tefnut (Tefnut is the rain deity in Ancient Egyptian religion.), a novel system that exploits opportunistically crowdsourced in-vehicle audio clips from an alternative, nowadays omnipresent source, smartphones, to achieve precise detection of rain leveraging a supervised recognizer constructed from a series of refined features. We conduct extensive experiments, and evaluation results demonstrate that Tefnut can detect the rain with 96.0% true positive rate, when deciding with a one-second-long in-vehicle audio segment only.
KW - Rain detection
KW - Signal processing
KW - Smartphone
KW - Supervised classification
UR - https://www.scopus.com/pages/publications/84981344775
U2 - 10.1007/978-3-319-42836-9_2
DO - 10.1007/978-3-319-42836-9_2
M3 - Conference contribution
AN - SCOPUS:84981344775
SN - 9783319428352
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 23
BT - Wireless Algorithms, Systems, and Applications - 11th International Conference, WASA 2016, Proceedings
A2 - Challal, Yacine
A2 - Yang, Qing
A2 - Yu, Wei
PB - Springer Verlag
Y2 - 8 August 2016 through 10 August 2016
ER -