Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel.
IEEE Trans Image Process. 2013 Jan;22(1):104-18. doi: 10.1109/TIP.2012.2197005. Epub 2012 May 1.
We address the challenging open problem of blindly separating time/position varying mixtures, and attempt to separate the sources from such mixtures without having prior information about the sources or the mixing system. Unlike studies concerning instantaneous or convolutive mixtures, we assume that the mixing system (medium) is varying in time/position. Attempts to solve this problem have mostly utilized, so far, online algorithms based on tracking the mixing system by methods previously developed for the instantaneous or convolutive mixtures. In contrast with these attempts, we develop a unified approach in the form of staged sparse component analysis (SSCA). Accordingly, we assume that the sources are either sparse or can be "sparsified." In the first stage, we estimate the filters of the mixing system, based on the scatter plot of the sparse mixtures' data, using a proper clustering and curve/surface fitting. In the second stage, the mixing system is inverted, yielding the estimated sources. We use the SSCA approach for solving three types of mixtures: time/position varying instantaneous mixtures, single-path mixtures, and multipath mixtures. Real-life scenarios and simulated mixtures are used to demonstrate the performance of our approach.
我们解决了盲目分离时变/位置变混合体的难题，并试图在没有源或混合系统的先验信息的情况下从这些混合体中分离出源。与涉及瞬时或卷积混合的研究不同，我们假设混合系统（介质）随时间/位置而变化。迄今为止，解决这个问题的尝试主要利用了基于先前为瞬时或卷积混合物开发的方法来跟踪混合系统的在线算法。与这些尝试相反，我们以分阶段稀疏成分分析 (SSCA) 的形式开发了一种统一的方法。因此，我们假设源要么是稀疏的，要么可以“稀疏化”。在第一阶段，我们根据稀疏混合物数据的散点图，使用适当的聚类和曲线/曲面拟合，估计混合系统的滤波器。在第二阶段，混合系统被反转，得到估计的源。我们使用 SSCA 方法解决三种类型的混合物：时变/位置变瞬时混合物、单路径混合物和多路径混合物。使用实际场景和模拟混合物来演示我们方法的性能。
