论文标题
基于辅助功能的独立矢量提取的块坐标下降算法
Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction
论文作者
论文摘要
在本文中，我们解决了从线性混合物中提取所有超级高斯源信号的问题，在该混合物中，（i）超级高斯来源$ k $的数量少于传感器$ m $的数量，并且（ii）最多有不需要提取的$ m-k $ sentary高斯声音。为了解决这个问题，已经开发了使用多数化最小化和块坐标下降（BCD）算法的独立向量提取（IVE），从而达到了可靠的源提取和低计算成本。在这里，我们通过仔细利用高斯噪声组件的平稳性来改善IVE的常规BCD。我们还新开发了一个半盲IVE的BCD，在该BCD中，几个超级高斯来源的传输函数先验。两种算法都由封闭式公式和广义特征值分解组成。在一个从嘈杂混合物中提取语音信号的数值实验中，我们表明，在半盲案例中给出了盲箱中的$ k = 1 $或至少$ k -1 $转移功能时，我们提出的BCD的收敛性要比常规的BCD的速度要快得多。
In this paper, we address the problem of extracting all super-Gaussian source signals from a linear mixture in which (i) the number of super-Gaussian sources $K$ is less than that of sensors $M$, and (ii) there are up to $M - K$ stationary Gaussian noises that do not need to be extracted. To solve this problem, independent vector extraction (IVE) using a majorization minimization and block coordinate descent (BCD) algorithms has been developed, attaining robust source extraction and low computational cost. We here improve the conventional BCDs for IVE by carefully exploiting the stationarity of the Gaussian noise components. We also newly develop a BCD for a semiblind IVE in which the transfer functions for several super-Gaussian sources are given a priori. Both algorithms consist of a closed-form formula and a generalized eigenvalue decomposition. In a numerical experiment of extracting speech signals from noisy mixtures, we show that when $K = 1$ in a blind case or at least $K - 1$ transfer functions are given in a semiblind case, the convergence of our proposed BCDs is significantly faster than those of the conventional ones.
