Glimpsing IVA: A Framework for Overcomplete/Complete/Undercomplete Convolutive Source Separation

Abstract--Independent vector analysis (IVA) is a method for separating convolutedly mixed signals that significantly reduces the occurrence of the well-known permutation problem in frequency domain blind source separation (BSS). In this paper, we develop a novel IVA-based unifying framework for overcomplete/complete/ undercomplete convolutive noisy BSS. We show that in order for the sources to be separable in the frequency domain, they must have a temporal dynamic structure. We exploit a common form of dynamics, especially present in speech, wherein the signals have silence periods intermittently, hence varying the set of active sources with time. This feature is extremely useful in dealing with overcomplete situations. An approach using hidden Markov models (HMMs) is proposed that takes advantage of different combinations of silence gaps of the source signals at each time period. This enables the algorithm to "glimpse" or listen in the gaps, hence compensating for the global...

• Type: Journal
• Year: 2010
• Where: TASLP
• Authors: Alireza Masnadi-Shirazi, Wenyi Zhang, Bhaskar D. Rao
• Keywords: Convolutive Noisy Bss, Frequency Domain, IVA-based Unifying Framework, Software Engineering, TASLP 2010