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Adaptive fusion of dictionary learning and multichannel BSS

Abolghasemi, V, Ferdowsi, S, Makkiabadi, B, Sanei, S, Abolghasemi, V and Makkiabadi, B (2012) Adaptive fusion of dictionary learning and multichannel BSS ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp. 2421-2424.

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Abstract

Sparsity has been shown to be very useful in blind source separation. However, in most cases the sources of interest are not sparse in their current domain and are traditionally sparsified using a predefined transform or a learned dictionary. In this paper, we address the case where the underlying sparse domains of the sources are not available and propose a solution via fusing the dictionary learning into the source separation. In the proposed method, a local dictionary is learned for each source along with separation and denoising of the sources. This iterative procedure adapts the dictionaries to the corresponding sources which consequently improves the quality of source separation. The results of our experiments are promising and confirm the strength of the proposed approach. © 2012 IEEE.

Item Type: Article
Authors :
NameEmailORCID
Abolghasemi, VUNSPECIFIEDUNSPECIFIED
Ferdowsi, SUNSPECIFIEDUNSPECIFIED
Makkiabadi, BUNSPECIFIEDUNSPECIFIED
Sanei, SUNSPECIFIEDUNSPECIFIED
Abolghasemi, VUNSPECIFIEDUNSPECIFIED
Makkiabadi, BUNSPECIFIEDUNSPECIFIED
Date : 2012
Identification Number : 10.1109/ICASSP.2012.6288404
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:13
Last Modified : 31 Oct 2017 14:55
URI: http://epubs.surrey.ac.uk/id/eprint/742475

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