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. © 2008 IEEE.
CITATION STYLE
Chen, G., Wang, T. J., & Herrera, P. (2008). A novel music retrieval system with relevance feedback. In 3rd International Conference on Innovative Computing Information and Control, ICICIC’08. https://doi.org/10.1109/ICICIC.2008.69
Mendeley helps you to discover research relevant for your work.