Glimpsing independent vector analysis: Separating more sources than sensors using active and inactive states

In this paper, we explore the problem of separating convolutedly mixed signals in the overcomplete (degenerate) case of having more sources than sensors. We exploit a common form of nonstationarity, especially present in speech, wherein the signals have silence periods intermittently, hence varying the set of active sources with time. A novel approach is proposed that takes advantage of different combinations of silence gaps in the source signals at each time period. This enables the algorithm to “glimpse” or listen in the gaps, hence compensating for the global degeneracy by allowing it to learn the mixing matrices at periods where it is locally less degenerate. Experiments using simulated and real room recordings were carried out yielding good separation results.