Independent vector analysis incorporating active and inactive states

Authors:
Alireza Masnadi-Shirazi;Bhaskar Rao
Affiliations:
Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA;Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Glimpsing IVA: a framework for overcomplete/complete/undercomplete convolutive source separation

IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications

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Abstract

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.