音频特征提取的混合独立分量分析和粗糙集方法

Xin He, Ling Guo, Jianyu Wang, Xianzhong Zhou
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摘要

音频分类基于音频特征。音频特征的选择可以在时间和频率时间上反映重要的音频分类特征。音频特征的提取和分析是音频分类的基础和重要内容。最重要的问题是如何有效地提取音频特征,并使它们相互独立,以减少信息冗余。本文将独立分量分析和粗糙集相结合,提出了一种音频特征提取方法,并通过实验证明了该方法具有较好的提取效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Independent Component Analysis and Rough Set Approach for Audio Feature Extraction
Audio classification is based on audio features. The choice of audio features can reflect important audio classification features in time and frequency time. The extraction and analysis of audio features are the base and important of audio classification. The most important problem is to extract audio features effectively and make them mutual independence to reduce information redundancy. In this paper, combined with independent component analysis and rough set, a method for audio feature extraction is presented and it's proved better performance by experiments.
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