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偏亚高斯语音信号有效分离ICA方法研究 被引量：3

Method Research on Effictive Seperated ICA Algorithm to Sub-Gaussian Distribution Audio Signal
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摘要 针对扩展Infomax语音分离算法仅只对无偏信号的概率密度分布进行建模的不足,提出了一种有效分离的有偏亚高斯信号ICA算法。通过修改扩展Informix算法所基于的Pearson混合模型,使修改后的模型既能较好地逼近对称的概率密度分布,又能逼近非对称的概率密度分布,从而在源信号是非对称分布的情况下,能获得更好的分离质量和较快的收敛速度。 An effictive seperated independent component analysis （ICA） algorithm is obtained by introducing a skewness-adjusting parameter to the Pearson mixture density model in extended Infomax algorithm. This model with skewness-adjusting parameter can cover a wider range of sub-Gaussian distribution including asymmetrical and multi-modal ones, resulting in more precisely approximating source＇s density. When dealing with non-skewed mixed sources, the new algorithm can achieve less steady-state error while maintaining fast convergence speed.
作者 洪英 韩周安
机构地区 北京理工大学信息科学技术学院 电子科技大学电子工程学院
出处 《电子科技大学学报》 EI CAS CSCD 北大核心 2008年第5期693-697,共5页 Journal of University of Electronic Science and Technology of China
关键词 语音分离 ICA算法 独立分量分析 Infomax算法 audio separation ICA algorithm independent component analysis infomax algorithm
分类号 TN912.16 [电子电信—通信与信息系统]
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参考文献11

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共引文献18

同被引文献27

  • 1王晓伟,林锁.基于独立分量分析的混合声音信号分离[J].兵工自动化,2007,26(6):48-50. 被引量：3
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引证文献3

二级引证文献8

电子科技大学学报
2008年 第5期

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