Independent vector analysis incorporating active and inactive states

Independent vector analysis (IVA) is a method for separating convolutedly mixed signals that avoids the well-known permutation problem in frequency domain blind source separation (BSS). In this paper, we exploit the nonstationarity of signals, a common feature, for BSS. One common type of nonstationarity, especially in speech, is that the signal can have silence periods intermittently, hence varying the set of active sources with time. To deal with such situations, we propose a novel state-based IVA algorithm. Moreover, we consider additive noise in our model. Computer simulations are conducted to compare the proposed method with the standard IVA and the results compare favorably.