@inproceedings{00dde6f95b0248a7a24e4d8ff82abc7a,
title = "Towards in time music mood-mapping for drivers: A novel approach",
abstract = "Road safety is a huge concern due to the large number of fatalities and injuries caused by road accidents. Research has shown that fatigue can adversely affect driving performance and increase risk of road accidents. It has been shown that driving performance is enhanced by stress-relieving music which thereby promotes safer driving. Context-aware music delivery systems promote safer driving through intelligent music recommendations based on contextual knowledge. Two key aspects of situation-aware music delivery are effectiveness and efficiency of music recommendation. Efficiency is a critical aspect in real-time context based music recommendation as the music delivery system should quickly sense any change in the situation and deliver suitable music before the sensed context-data becomes obsolete. We focus on the efficiency of situation-aware music delivery systems in this paper. Music mood-mapping is a process which helps in understanding the mood of a song and is hence used in situation-aware music recommendation systems. This process requires a large processing time due to the complex calculations and large sizes of music files involved. Hence, optimizing this process is the key to improving the efficiency of context-aware music delivery systems. Here, we propose a novel cloud and crowd-sensing based approach to considerably optimize the efficiency of situation-aware music delivery systems.",
keywords = "Cloud, Context-aware, Crowd-sensing, Mood-mapping, Music matching, Music recommendation, Offloading, Vehicular sensor application",
author = "Krishnan, {Arun Sai} and Xiping Hu and Deng, {Jun Qi} and Li Zhou and Ngai, {Edith C.H.} and Xitong Li and Leung, {Victor C.M.} and Kwok, {Yu Kwong}",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2015 ; Conference date: 02-11-2015 Through 06-11-2015",
year = "2015",
month = nov,
day = "2",
doi = "10.1145/2815347.2815352",
language = "English",
series = "DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications",
publisher = "Association for Computing Machinery, Inc",
pages = "59--66",
booktitle = "DIVANet 2015 - Proceedings of the 5th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications",
}