Bayesian noisy ICA for source switching environments
J. Hirayama, Shin‐ichi Maeda, Shin Ishii · IEEE/SP 13th Workshop on Statistical Signal Processing, 2005 · 2005
Most of the existing algorithms for blind source separation (BSS) assume that the number of sources is known and constant for all samples. Real situations, however, often have difficult non-stationarity such that each source signal abruptly switches to appear or disappear and hence the number of sources varies with time. In this article, we propose a noisy independent component analysis (ICA) algorithm that assumes unknown and varying number of sources. We employ Bayesian variable selection in combination with the hidden Markov model to automatically select and switch the set of sources which are temporally active in a certain period. We formulate our algorithm based on the Bayesian inference using the variational Bayes method. A simulation study using artificial data showed that our approach successfully recovered source signals even when the number of sources varied with time