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A Novel Music Retrieval System with Relevance Feedback
| Title | A Novel Music Retrieval System with Relevance Feedback |
| Publication Type | Conference Paper |
| Year of Publication | 2008 |
| Conference Name | Third International Conference on Innovative Computing, Information and Control |
| Authors | Chen, G. , Wang T. - G. , & Herrera P. |
| Abstract | Although 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 document | http://mtg.upf.edu/files/publications/Chen-ICICIC-2008.pdf |
