The relevance feedback is proved to be an effective method in text information, image, and video retrievals. In this paper, we introduce this technique to carry out audio retrieval, in a hope not only to enhance the retrieval performance but also through this kind of user interaction to enhance the searching ability. Based on an initial searching result, a user can tag files with relevance or irrelevance according to one's judgment and preference. Then, the system updates the weights in similarity measurement and/or the query itself based on the feedbacks. Two relevance feedback algorithms have been proposed. One is a simplified technique used for feedback in image retrieval; another is based on constrained optimization concept. Experiments show that both approaches can yield similar performance improvements. Furthermore, the latter one can utilize negative feedbacks in a unified approach as well.
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