KERNEL-BASED AUDIO CLASSIFICATION

摘要：

Audio classification is subject to the heavy computation because of the high dimensionality of audio features as well as the unfixed length of audio segments. In this paper, an audio classification method based on the kernel is proposed, which could significantly reduce the dimensionality of audio features,and convert the variable length audio segments to fixed one.Gaussian Fisher Kernel is employed for transforming the audio clip to the equivalent parameter space, which bears the characteristic of low dimensionality. Audio reduct is extracted by the method of Variable Precision Rough Set Model, and it has the strong discrimination ability and could serve as the proxy of audio clip. Audio retrieval experiments show that our method could achieve the more accurate classification than conventional methods.

关键词： Audio classification gaussian fisher kernel variable precision rough sets

作者: XIAO-LI LI ZHEN-LONG DU YA-FEN ZHANG

作者单位: Computer & Communication School, Lanzhou University of Science&Technology, Lanzhou 730050

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3313-3316

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）