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Audio Segmentation via the Similarity Measure of Audio Feature Vectors
作者姓名:CHEN  Gang  TAN  Hui  CHEN  Xin-meng
作者单位:[1]School of Computer, Wuhan University, Wuhan 430072, Hubei, China
基金项目:Supported by the National Natural Science Foundation of China (10371033)
摘    要:A formula to compute the similarity between two audio feature vectors is proposed, which can map arbitrary pair of vectors with equivalent dimension to 0,1). To fulfill the task of audio segmentation, a self-similarity matrix is computed to reveal the inner structure of an audio clip to be segmented. As the final result must be consistent with the subjective evaluation and be adaptive to some special applications, a set of weights is adopted, which can be modified through relevance feedback techniques. Experiments show that satisfactory result can be achieved via the algorithm proposed in this paper.

关 键 词:音频分割  测量方法  自模仿矩阵  反馈系统
文章编号:1007-1202(2005)05-0833-05
收稿时间:2004-10-20

Audio segmentation via the similarity measure of audio feature vectors
CHEN Gang TAN Hui CHEN Xin-meng.Audio Segmentation via the Similarity Measure of Audio Feature Vectors[J].Wuhan University Journal of Natural Sciences,2005,10(5):833-837.
Authors:Chen Gang  Tan Hui  Chen Xin-meng
Institution:(1) School of Computer, Wuhan University, 430072 Wuhan, Hubei, China
Abstract:A formula to compute the similarity between two audio feature vectors is proposed, which can map arbitrary pair of vectors with equivalent dimension to \0,1). To fulfill the task of audio segmentation, a self-similarity matrix is computed to reveal the inner structure of an audio clip to be segmented. As the final result must be consistent with the subjective evaluation and be adaptive to some special applications, a set of weights is adopted, which can be modified through relevance feedback techniques. Experiments show that satisfactory result can be achieved via the algorithm proposed in this paper.
Keywords:audio segmentation  abrupt change detection  overall error  similarity measure  self-similarity matrix  relevance feedback
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