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Methods for learning adaptive dictionary in underdetermined speech separation

Xu, T and Wang, W (2011) Methods for learning adaptive dictionary in underdetermined speech separation In: 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011-09-18 - 2011-09-21, Beijing, China.

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Abstract

Underdetermined speech separation is a challenging problem that has been studied extensively in recent years. A promising method to this problem is based on the so-called sparse signal representation. Using this technique, we have recently developed a multi-stage algorithm, where the source signals are recovered using a pre-defined dictionary obtained by e.g. the discrete cosine transform (DCT). In this paper, instead of using the pre-defined dictionary, we present three methods for learning adaptive dictionaries for the reconstruction of source signals, and compare their performance with several state-of-the-art speech separation methods. © 2011 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Xu, TUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Date : 31 October 2011
Identification Number : 10.1109/MLSP.2011.6064610
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/PBLIEEE, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:43
Last Modified : 31 Oct 2017 14:37
URI: http://epubs.surrey.ac.uk/id/eprint/596096

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