Musemble: A music retrieval system based on learning environment

Seungmin Rho, Byeong J. Han, Een Jun Hwang, Minkoo Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Query reformulation has been suggested as an effective way to improve retrieval efficiency in text information retrieval and one of the well-known techniques for query reformulation is user relevance feedback. Recently, there has been an increased interest in the query reformulation using relevance feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have still not been exploited widely in the field of music retrieval. In this paper, we propose a novel music retrieval scheme that is based on user relevance feedback with genetic algorithm and evolutionary method with neural network. The former is for reformulating a user query and the latter is for reducing the population size by learning neural network. We implemented a prototype music retrieval system called MUSEMBLE based on this scheme. Experimental results showed that our proposed scheme achieves a good performance.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
Pages1463-1466
Number of pages4
Publication statusPublished - 2007 Dec 1
EventIEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China
Duration: 2007 Jul 22007 Jul 5

Other

OtherIEEE International Conference onMultimedia and Expo, ICME 2007
CountryChina
CityBeijing
Period07/7/207/7/5

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ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software

Cite this

Rho, S., Han, B. J., Hwang, E. J., & Kim, M. (2007). Musemble: A music retrieval system based on learning environment. In Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007 (pp. 1463-1466). [4284937]