Title :
Methods for learning adaptive dictionary in underdetermined speech separation
Author :
Xu, Tao ; Wang, Wenwu
Author_Institution :
Centre for Vision, Speech & Signal Process., Univ. of Surrey, Guildford, UK
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.
Keywords :
signal reconstruction; source separation; speech processing; adaptive dictionary; discrete cosine transform; multistage algorithm; source signal reconstruction; sparse signal representation; underdetermined speech separation; Dictionaries; Discrete cosine transforms; Signal processing algorithms; Source separation; Speech; Vectors; Underdetermined blind speech separation; adaptive dictionary learning; sparse representation;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on
Conference_Location :
Santander
Print_ISBN :
978-1-4577-1621-8
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2011.6064610