Paper Info
Title
Blind speech separation for convolutive mixtures using an oriented principal components analysis method
Abstract
This paper deals with blind speech separation of convolutive mixtures of sources. The separation criterion is based on the Oriented Principal Components Analysis (OPCA) method. OPCA is a (second order) extension of standard Principal Component Analysis (PCA) aiming at maximizing the power ratio of a pair of signals. The convolutive mixing is obtained by modeling the Head Related Transfer Function (HRTF). Experimental results show the efficiency of the proposed approach in terms of subjective and objective evaluation, when compared to the widely used CFICA (Convolutive Fast-ICA) algorithm.
Year
Venue
Keywords
2010
Aalborg
blind source separation,principal component analysis,speech processing,transfer functions,hrtf,opca,blind speech separation,convolutive mixtures,head related transfer function,oriented principal components analysis method,blind source separation (bss),oriented principal component analysis,convolutive mixture,speech signals,speech,frequency domain analysis
Field
DocType
ISSN
Frequency domain,Speech processing,Head-related transfer function,Power ratio,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Blind speech separation,Blind signal separation,Principal component analysis
Conference
2219-5491
Citations 
PageRank 
References 
2
0.37
7
Authors
3
Name
Order
Citations
PageRank
Yasmina Benabderrahmane141.51
Sid-Ahmed Selouani2155.71
Douglas O’Shaughnessy3103.37