Individualization of music similarity perception via feature subset selection

We address the problem of modeling the subjective perception of similarity between two music files that have been extracted from a music database with use of objective features. We propose the importation of user models in content-based music retrieval systems, which embody the ability of evolving and using different music similarity measures for different users. Specifically, a user-supplied relevance feedback procedure allows the system to determine which subset of a set of objective features approximates more efficiently the subjective music similarity of a specific user. Our implementation of the proposed system verifies our hypothesis and exhibits significant improvement in perceived music similarity