Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yeh, Chan-Chang | en_US |
dc.contributor.author | Tseng, Shian-Shyong | en_US |
dc.contributor.author | Tsai, Pei-Chin | en_US |
dc.contributor.author | Weng, Jui-Feng | en_US |
dc.date.accessioned | 2014-12-08T15:24:37Z | - |
dc.date.available | 2014-12-08T15:24:37Z | - |
dc.date.issued | 2006 | en_US |
dc.identifier.isbn | 3-540-48766-2 | en_US |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/17086 | - |
dc.description.abstract | With the development of multimedia technology, research on music is getting more and more popular. Nowadays researchers focus on studying the relationship between music and listeners' emotions but they didn't consider users' differences. Therefore, we propose a Personalized Music Emotion Prediction (P-MEP) System to assist predicting listeners' music emotion concerning with users' differences. To analyze listeners' emotional response to music, the P-MEP rules will be generated in the analysis procedure consisting of 5 phases. During the application procedure, the P-MEP System predicts the new listener's emotional response to music. The result of the experiment shows that the generated P-MEP rules can be used to predict emotional response to music concerning with listeners' differences. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | personalized music emotion prediction | en_US |
dc.subject | data mining | en_US |
dc.subject | classification | en_US |
dc.subject | clustering | en_US |
dc.title | Building a personalized music emotion prediction system | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | Advances in Multimedia Information Processing - PCM 2006, Proceedings | en_US |
dc.citation.volume | 4261 | en_US |
dc.citation.spage | 730 | en_US |
dc.citation.epage | 739 | en_US |
dc.contributor.department | 資訊工程學系 | zh_TW |
dc.contributor.department | Department of Computer Science | en_US |
dc.identifier.wosnumber | WOS:000243129600084 | - |
Appears in Collections: | Conferences Paper |