A Novel Music Retrieval System with Relevance Feedback

TitleA Novel Music Retrieval System with Relevance Feedback
Publication TypeConference Paper
Year of Publication2008
Conference NameThird International Conference on Innovative Computing, Information and Control
AuthorsChen, G., Wang T. - G., & Herrera P.
AbstractAlthough various researches have been conducted in the area of content-based music retrieval, however, few works have been done using relevance feedback for improving the retrieval performance. In this paper we introduce a novel content-based music retrieval system with relevance feedback. It enables users to search favorite music files by introducing the user as a part of the retrieval loop. In our system, we used a radial basis function (RBF) based learning algorithm and a method exploited both positive and negative examples to reweight feature components. Experiments evaluate the performance of the proposed approach and prove the effectiveness of our system.
preprint/postprint documenthttp://mtg.upf.edu/files/publications/Chen-ICICIC-2008.pdf