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The segments are defined as short terms of video contents that have various time lengths.
Therefore, the proposed method enables calculation of the correlation between human motions and music pieces that have various time lengths.
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.
However, there have been no methods focused on handling data that have various time lengths, i.e., human motions and music pieces.
Consequently, effective modeling of the relationship using music and human motion data that have various time lengths is realized, and successful music recommendation can be expected.
Since human motions and music pieces have various time lengths, it is necessary to calculate the correlation between time series having different lengths.
Similar(50)
The above-kernel function can be used for time series having various time lengths.
Then, the proposed method introduces the kernel functions that can be used for time series having various time lengths based on the LCSS or p-spectrum.
Then, effective modeling of the relationship using human motions and music pieces having various time lengths is realized, and successful music recommendation can be expected.
In this approach, we define new kernel functions that can be used for data having various time lengths and introduce them into the kernel CCA. Figure 2 Overview of the proposed method.
Specifically, we newly adopt two types of kernel functions, which can represent similarities by effectively using human motions or music pieces having various time lengths, for the kernel CCA-based correlation calculation.
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Justyna Jupowicz-Kozak
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