Presentation 2010-06-21
Music Recommendation According to Human Motion Based on Kernel CCA
Hiroyuki OHKUSHI, Takahiro OGAWA, Miki HASEYAMA,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) This paper proposes a method for recommendation of suitable music for human motion based on kernel canonical correlation analysis (CCA). The kernel CCA is used to find the relationship between different data sets, human motion data and music data. In this approach, the proposed method newly uses similarity of human motions, which is robust to temporal expantions of the motion data, as the kernel function. Therefore, we can successfully model the relationship between the human motion and the music to recommend suitable music for human motions. Experimental results are shown to verify the performance of the proposed method.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) music recommendation / kernel CCA / motion feature / music feature
Paper # CAS2010-12,VLD2010-22,SIP2010-33,CST2010-12
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Conference Information
Committee VLD
Conference Date 2010/6/14(1days)
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Paper Information
Registration To VLSI Design Technologies (VLD)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Music Recommendation According to Human Motion Based on Kernel CCA
Sub Title (in English)
Keyword(1) music recommendation
Keyword(2) kernel CCA
Keyword(3) motion feature
Keyword(4) music feature
1st Author's Name Hiroyuki OHKUSHI
1st Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University()
2nd Author's Name Takahiro OGAWA
2nd Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University
3rd Author's Name Miki HASEYAMA
3rd Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University
Date 2010-06-21
Paper # CAS2010-12,VLD2010-22,SIP2010-33,CST2010-12
Volume (vol) vol.110
Number (no) 87
Page pp.pp.-
#Pages 6
Date of Issue