Kernel CCA-based music recommendation according to human motion robust to temporal expansion

This paper proposes a method for kernel canonical correlation analysis (CCA) based music recommendation robust to temporal expansion. Kernel CCA is used to find the relationship between different data sets. Generally, since motions and music pieces in video sequences have various time lengths, it is necessary to allow the internal temporal expansion of the data. Our kernel CCA-based music recommendation method uses similarities of human motions and music pieces, which are robust to internal temporal expansions. Then this approach enables successful extraction of the relationship between these data to recommend a music piece suitable for human motions. Experimental results are shown to verify the performance of the proposed method.