Independent vector analysis for source separation using a mixture of gaussians prior.

Neural Computation
Jiucang HaoTerrence J Sejnowski

Abstract

Convolutive mixtures of signals, which are common in acoustic environments, can be difficult to separate into their component sources. Here we present a uniform probabilistic framework to separate convolutive mixtures of acoustic signals using independent vector analysis (IVA), which is based on a joint distribution for the frequency components originating from the same source and is capable of preventing permutation disorder. Different gaussian mixture models (GMM) served as source priors, in contrast to the original IVA model, where all sources were modeled by identical multivariate Laplacian distributions. This flexible source prior enabled the IVA model to separate different type of signals. Three classes of models were derived and tested: noiseless IVA, online IVA, and noisy IVA. In the IVA model without sensor noise, the unmixing matrices were efficiently estimated by the expectation maximization (EM) algorithm. An online EM algorithm was derived for the online IVA algorithm to track the movement of the sources and separate them under nonstationary conditions. The noisy IVA model included the sensor noise and combined denoising with separation. An EM algorithm was developed that found the model parameters and separated th...Continue Reading

References

Nov 1, 1995·Neural Computation·A J Bell, T J Sejnowski
Feb 9, 1999·Neural Computation·J F Cardoso
May 5, 1999·Neural Computation·H Attias
Jul 7, 2001·Neural Computation·A HyvärinenM Inki
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Aug 19, 2005·Neural Computation·Kaare Brandt PetersenLars Kai Hansen
Feb 7, 2008·IEEE Transactions on Neural Networks·A Hyvärinen

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Citations

Aug 10, 2011·Biomedizinische Technik. Biomedical Engineering·Fengyu CongTapani Ristaniemi
Dec 11, 2013·Neural Computation·T J Zeng, Q Y Feng

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