Markov and Semi-Markov Switching of Source Appearances for Nonstationary Independent Component Analysis
Markov and Semi-Markov Switching of Source Appearances for Nonstationary Independent Component Analysis
复制标题
DOI：
10.1109/tnn.2007.895829
复制
发表时间：
2007-09
影响因子：
--
通讯作者：
J. Hirayama;S. Maeda;S. Ishii
中科院分区：
文献类型：
--
作者：
J. Hirayama;S. Maeda;S. Ishii
独立分量分析（伊卡）是目前盲源分离（BSS）中最常用的方法，盲源分离是指在观测到未知源信号的混合信号而实际混合过程未知的情况下，恢复未知源信号的问题。许多伊卡算法假设一组固定的源信号始终存在于整个时间序列的混合物中进行检查。然而，现实世界的信号往往有这样困难的非平稳性，每个源信号突然出现或消失，因此活动源的集合动态地随时间变化。在本文中，我们提出了开关伊卡（SwICA），它专注于这种情况。所提出的方法是基于噪声伊卡制定为一个生成模型。我们采用一种特殊类型的隐马尔可夫模型（HMM）来表示这种先验知识，源可能会突然出现或消失的时间。然后，特殊的HMM设置以动态方式提供变量选择的效果。我们使用变分贝叶斯（VB）的方法来推导出一个有效的近似贝叶斯推断这个模型。在仿真实验中，使用人工和现实的源信号，所提出的方法表现出优于现有的方法，特别是在噪声的存在下，上级的性能。比较的方法包括与非完整约束的自然梯度伊卡，和现有的伊卡方法，将HMM源模型，其目的是处理一般的非平稳性，可能存在于源信号。此外，该方法可以成功地恢复源信号，即使在真源的总数被高估或大于混合物的总数。我们还提出了一个修改的基本马尔可夫模型的半马尔可夫模型，并表明半马尔可夫模型是更有效的源外观的鲁棒估计。
Independent component analysis (ICA) is currently the most popularly used approach to blind source separation (BSS), the problem of recovering unknown source signals when their mixtures are observed but the actual mixing process is unknown. Many ICA algorithms assume that a fixed set of source signals consistently exists in mixtures throughout the time-series to be examined. However, real-world signals often have such difficult nonstationarity that each source signal abruptly appears or disappears, thus the set of active sources dynamically changes with time. In this paper, we propose switching ICA (SwICA), which focuses on such situations. The proposed approach is based on the noisy ICA formulated as a generative model. We employ a special type of hidden Markov model (HMM) to represent such prior knowledge that the source may abruptly appear or disappear with time. The special HMM setting then provides an effect of variable selection in a dynamic way. We use the variational Bayes (VB) method to derive an effective approximation of Bayesian inference for this model. In simulation experiments using artificial and realistic source signals, the proposed method exhibited performance superior to existing methods, especially in the presence of noise. The compared methods include the natural-gradient ICA with a nonholonomic constraint, and the existing ICA method incorporating an HMM source model, which aims to deal with general nonstationarities that may exist in source signals. In addition, the proposed method could successfully recover the source signals even when the total number of true sources was overestimated or was larger than that of mixtures. We also propose a modification of the basic Markov model into a semi-Markov model, and show that the semi-Markov one is more effective for robust estimation of the source appearance.