Weight updating for relevance feedback in audio retrieval

The relevance feedback is proved to be an effective method in text information, image, and video retrieval. 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 feedback. 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 feedback in a unified approach as well.