University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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.

[img] Text
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (151kB)
[img] Text (licence)
Restricted to Repository staff only

Download (33kB)


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 :
Xu, T
Wang, W
Date : 31 October 2011
DOI : 10.1109/MLSP.2011.6064610
Contributors :
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:43
Last Modified : 31 Oct 2017 14:37

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800