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基于音频的电视新闻节目的主题检索和聚类

Audio-based Topic Retrieval and Clustering of TV Broadcasting News
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摘要 随着流媒体应用的蓬勃兴起,基于媒体内容的检索和管理逐渐成为当前的学术研究热点。新闻节目作为电视节目的一种常见形式,对其主题进行自动提取检索具有重要的实际意义。该文从电视新闻节目的音频入手,综合应用了播音室语音/非播音室语音分类、说话人转换点检测以及按说话人聚类等多种技术,实现了对电视新闻节目的主题的检索和聚类。实验表明,该文中的方法能够找到新闻节目中96%以上的播音室段落,并对其进行准确归类,显示了这种方法的可行性和潜在价值。 With boosting of stream media applications, content-based media information retrieval becomes hot topic of current academic research. Since news program is familiar and popular, topic retrieval of news program has important practical significance. Based on audio processing, this paper integrates studio / non-studio classification, speaker change detection and speaker clustering, and realizes automatic news topic retrieval and clustering according to anchorman. The experiment indicates that above 96% studio segments of news programs can be found out and clustered, and proves feasibility and potential of the method.
作者 王磊 杜利民 王劲林
机构地区 中国科学院声学研究所
出处 《电子与信息学报》 EI CSCD 北大核心 2007年第10期2498-2503,共6页 Journal of Electronics & Information Technology
关键词 新闻主题检索 音频分类 说话人检测 说话人聚类 贝叶斯信息准则 News topic retrieval Studio / non-studio classification Speaker change detection Speaker clustering Bayesian Information Criterion （BIC）
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献9

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电子与信息学报
2007年 第10期

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