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

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. Furtherm...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 74; no. 17; pp. 3713 - 3715
Main Authors Oh, Myungwoo, Park, Hyung-Min
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.10.2011
Subjects
Algorithms
Blind source separation
Blinds
Distortion
Exploitation
Feed-forward network
Independent component analysis
Learning
Networks
Separation
Vector analysis
Feed-forward network
Blind source separation
Independent component analysis
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Summary: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.
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2011.06.008