Independent Vector Analysis with Deep Neural Network Source Priors

08/23/2020 ∙ by Xi-Lin Li, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.