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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Music recommendation according to human motion based on kernel CCA-based relationship
This item is licensed under:Creative Commons Attribution 2.0 Generic
| Title: | Music recommendation according to human motion based on kernel CCA-based relationship |
| Authors: | Ohkushi, Hiroyuki Browse this author | | Ogawa, Takahiro Browse this author | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | content-based multimedia recommendation | | kernel canonical correlation analysis | | longest common subsequence | | p-spectrum |
| Issue Date: | 5-Dec-2011 |
| Publisher: | Springer |
| Journal Title: | EURASIP Journal on Advances in Signal Processing |
| Volume: | 2011 |
| Start Page: | 121 |
| Publisher DOI: | 10.1186/1687-6180-2011-121 |
| Abstract: | In this article, a method for recommendation of music pieces according to human motions based on their kernel canonical correlation analysis (CCA)-based relationship is proposed. In order to perform the recommendation between different types of multimedia data, i.e., recommendation of music pieces from human motions, the proposed method tries to estimate their relationship. Specifically, the correlation based on kernel CCA is calculated as the relationship in our method. Since human motions and music pieces have various time lengths, it is necessary to calculate the correlation between time series having different lengths. Therefore, new kernel functions for human motions and music pieces, which can provide similarities between data that have different time lengths, are introduced into the calculation of the kernel CCA-based correlation. This approach effectively provides a solution to the conventional problem of not being able to calculate the correlation from multimedia data that have various time lengths. Therefore, the proposed method can perform accurate recommendation of best matched music pieces according to a target human motion from the obtained correlation. Experimental results are shown to verify the performance of the proposed method. |
| Rights: | http://creativecommons.org/licenses/by/2.0 |
| Type: | article |
| URI: | http://hdl.handle.net/2115/48652 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 長谷山 美紀
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