Blind source separation based on independent vector analysis using feed-forward network

초록

This paper presents an algorithm that employs a feed-forward (FF) network on each bin as an unmixing system in the framework of independent vector analysis (IVA) to effectively separate highly reverberated mixtures with the exploitation of inter-frequency dependencies of each source signal. Furthermore, to avoid whitening of unmixed source signals due to the use of the FF unmixing network, we derive a learning algorithm for the network based on the extended non-holonomic constraint and the minimal distortion principle. Experiments show that the proposed method delivers better separation performance than the conventional IVA and the FF independent component analysis methods. (C) 2011 Elsevier B.V. All rights reserved.

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