Equivariant nonstationary source separation

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. [Neural Networks 8 (1995) 411] and derive natural gradient learning algorithms for both fully connected recurrent network and feedforward network. Since our algorithms employ the natural gradient method, they possess the equivariant property and

• Type: Journal
• Year: 2002
• Where: NN
• Authors: Seungjin Choi, Andrzej Cichocki, Shun-ichi Amari
• Keywords: Natural Gradient, Neural Networks, NN 2002, Nonstationary Sources