MUSEMBLE: A Music Retrieval System Based on Learning Environment

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.