A near real-time approach for convolutive blind source separation
A near real-time approach for convolutive blind source separation
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DOI：
10.1109/tcsi.2005.854295
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发表时间：
2006-01
期刊：
影响因子：
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通讯作者：
Shuxue Ding;Jie Huang;D. Wei;A. Cichocki
中科院分区：
文献类型：
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作者：
Shuxue Ding;Jie Huang;D. Wei;A. Cichocki
在本文中，我们提出了一种卷积盲源分离（CBSS）的实时信号处理算法，这是一种很有前途的声源分离技术，在现实环境中，如房间/办公室或车辆。首先，我们应用了最适合实时CBSS处理的重叠保存（重叠滑动窗口）策略；这种方法还可以帮助解决排列问题。其次，我们考虑了在频域中分离源的问题。我们引入了一个改进的观测信号的相关矩阵，并通过矩阵的对角化来实现CBSS。第三，我们提出了一种通过求解CBSS的正态方程来对角化修正后的相关矩阵的方法。我们提出的算法的一个理想特征是它可以显式地解决CBSS问题，而不是像传统算法那样随机地解决CBSS问题。此外，还可以实时分离源的卷积混合。我们设计了几个模拟来比较我们的算法与相应的基于梯度的方法的有效性。与基于梯度的方法相比，我们提出的算法具有更好的收敛速度。我们还设计了一个实验来测试该算法在现实环境中分离声源的实时CBSS处理中的有效性。在此实验环境下，我们的算法的收敛时间明显快于基于梯度的算法。此外，与基于梯度的算法相比，我们的算法收敛到一个更小的代价函数值，从而保证了更好的性能。
In this paper, we propose an algorithm for real-time signal processing of convolutive blind source separation (CBSS), which is a promising technique for acoustic source separation in a realistic environment, e.g., room/office or vehicle. First, we apply an overlap-and-save (sliding windows with overlapping) strategy that is most suitable for real-time CBSS processing; this approach can also aid in solving the permutation problem. Second, we consider the issue of separating sources in the frequency domain. We introduce a modified correlation matrix of observed signals and perform CBSS by diagonalization of the matrix. Third, we propose a method that can diagonalize the modified correlation matrix by solving a so-called normal equation for CBSS. One desirable feature of our proposed algorithm is that it can solve the CBSS problem explicitly, rather than stochastically, as is done with conventional algorithms. Moreover, a real-time separation of the convolutive mixtures of sources can be performed. We designed several simulations to compare the effectiveness of our algorithm with its counterpart, the gradient-based approach. Our proposed algorithm displayed superior convergence rates relative to the gradient-based approach. We also designed an experiment for testing the efficacy of the algorithm in real-time CBSS processing aimed at separating acoustic sources in realistic environments. Within this experimental context, the convergence time of our algorithms was substantially faster than that of the gradient-based algorithms. Moreover, our algorithm converges to a much lower value of the cost function than that of the gradient-based algorithm, ensuring better performance.