Relevance Feedback in an Adaptive Space with One-Class SVM for Content-Based Music Retrieval

摘要：

In this paper, we develop a novel scheme to content-based music retrieval, using relevance feedback with One-class Support vector Machine (SVM). Since one-class SVM only concerns the relevant examples and neglects useful information from irrelevant examples provided by the user, an adaptive space is proposed using both relevant and irrelevant examples. The adaptive space, integrated with one-class SVM, transforms the feature space to a space that would better correspond to the users needs and specificities. Experimental results of retrieval on a music genre database demonstrate the effectiveness of our approach.

作者: Gang Chen Tianjiang Wang Perfecto Herrera

作者单位: Department of Computer Science, Huazhong University of Science and TechnologyWuhan, China Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain

会议类型: 国际会议

会议名称: 2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)

会议地点: 镇江

会议语种:英文

页码: 1153-1158

在线出版日期: 2008-07-07（万方平台首次上网日期，不代表论文的发表时间）