  This paper studies the density priors for independent vector analysis (IVA)
with convolutive speech mixture separation as the exemplary application. Most
existing source priors for IVA are too simplified to capture the fine
structures of speeches. Here, we first time show that it is possible to
efficiently estimate the derivative of speech density with universal
approximators like deep neural networks (DNN) by optimizing certain proxy
separation related performance indices. Experimental results suggest that the
resultant neural network density priors consistently outperform previous ones
in convergence speed for online implementation and signal-to-interference ratio
(SIR) for batch implementation.
