文章基本信息

标题：GA-Based Adaptive Window Length Estimation for Highly Accurate Audio Segmentation
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作者：Myeongsu Kang ; Jong-Myon Kim
期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2015
卷号：10
期号：1
页码：429-436
DOI：10.14257/ijmue.2015.10.1.39
出版社：SERSC
摘要：Accurate audio segmentation has recently received increasing attention for its applications in automatic indexing, content analysis and information retrieval. Hence, this paper proposes a highly accurate audio segmentation methodology using a genetic algorithm-based approach to adapting and optimizing segmentation window lengths. Specifically, this paper analyzes the parameter sequence of the root-mean-square values of an input audio stream with optimal sliding window (or segmentation window) lengths found and adapted by a genetic algorithm. In addition, this paper determines whether an audio-cut occurs or not by utilizing the parameter sequences as inputs of a support vector machine. Experimental results indicate that the proposed approach achieves 100.00% and 98.69% in the average precision and recall rates of segmentation performance, respectively.
关键词：Audio segmentation; genetic algorithm; support vector machine; parameter ; sequence