Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học được đăng trên tạp chí toán học quốc tế đề tài: Music recommendation according to human motion based on kernel CCA-based relationship | Ohkushi et al. EURASIP Journal on Advances in Signal Processing 2011 2011 121 http content 2011 1 121 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Music recommendation according to human motion based on kernel CCA-based relationship Hiroyuki Ohkushi Takahiro Ogawa and Miki Haseyama 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 . 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. Keywords content-based multimedia recommendation kernel canonical correlation analysis longest common subsequence p-spectrum 1 Introduction With the popularization of online digital media stores users can obtain various kinds of multimedia data. Therefore technologies for retrieving and recommending desired contents are necessary to satisfy the various demands of users. A .
