Relevance Feedback in An Adaptive Space with One-Class SVM for Content-Based Music Retrieval

TitleRelevance Feedback in An Adaptive Space with One-Class SVM for Content-Based Music Retrieval
Publication TypeConference Paper
Year of Publication2008
Conference NameIEEE International Conference on Audio, Language and Image Processing 2008
AuthorsChen, G., Wang T. - G., & Herrera P.
Conference Start Date07/03/2008
Conference LocationShangai, China
KeywordsMusic Retrieval, One-class SVM, Relevance Feedback
AbstractIn this paper, we develop a novel scheme to content-based music retrieval, using relevance feedback with One-class SVM. Since One-class SVM only concerns the relevant examples and neglects useful information from irrelevant examples provided by the user, an adaptive space is proposed using both relevant and irrelevant examples. The adaptive space, integrated with One-class SVM, transforms the feature space to a space that would better correspond to the user’s needs and specificities. Experimental results of retrieval on a music genre database demonstrate the effectiveness of our approach.
preprint/postprint documenthttp://mtg.upf.edu/files/publications/Chen-ICALIP-2008.pdf
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