Automatic music video generation based on temporal pattern analysis
Proceedings of the 12th annual ACM international conference on Multimedia
Information Filter for Ambiguous Information Retrieval
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Search in the mood: the information filter based on ambiguous queries
International Journal of Computer Applications in Technology
Music video viewers' preference evaluation criteria and their characteristics
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
A self-similarity approach to repairing large dropouts of streamed music
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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The purpose of this study is to classify viewers' favorite music videos based on viewers' attributes evaluation and identify their characteristics. In this study, we designed a web-based questionnaire survey to collect data from forty-three young people. Participants watched fifteen music videos one-by-one and rated overall preference and twelve attributes with five-point scales. Then, we carried out the k-means clustering analysis according to the viewers' attributes evaluation ratings. As a result, we found that there were three types of viewers' favorite music videos. Viewers gave high evaluations to those music videos primarily because: (1) auditory-leading type: they liked music and singer; (2) visual-leading type: they were amazed by the imaging technique; (3) synergic type: they got favorable impressions overall.allIt is noteworthy that stories related to the lyrics and time-order structured had especially a positive impact on the viewers' evaluation.