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基于非负矩阵分解的欠定卷积盲源分离方法 被引量：2

Underdetermined convolutive blind source separation algorithm based on nonnegative matrix factorization
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摘要 针对音频信号欠定卷积混合模型的盲源分离求解问题,提出一种基于非负矩阵分解(NMF)的盲源分离方法。该方法以板仓-斋藤(Itakura-Saito)散度和的最大值为目标函数,利用高斯分量表示源信号的短时傅里叶变换(STFT),使用乘积更新算法估计频域内的源信号,以提高其估计的准确度。仿真结果验证了该方法的有效性。 Aiming at underdetermined convolutive blind source separation, a method based on nonnegative matrix fac- torization （NMF） is provided. The STFT of each source signal is given by gaussian components, with the maxim of I.takura-Saito divergence as target function. The mutiplicative update （MU） is used to estimate the original signal in the frequency domain for improving the accuracy. The simulation result verifies the efficiency of the method.
作者 闵苏 刘庆华
机构地区 桂林电子科技大学信息与通信学院
出处 《桂林电子科技大学学报》 2013年第1期1-3,13,共4页 Journal of Guilin University of Electronic Technology
基金 国家自然科学基金(60972084) 广西自然科学基金(0832007Z)
关键词 欠定卷积 非负矩阵分解 乘积更新算法 underdetermined convolute nonnegative matrix factorization multiplicative update algorithm
分类号 TN911.7 [电子电信—通信与信息系统]
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参考文献9

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二级参考文献10

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桂林电子科技大学学报
2013年 第1期

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