Paper Info
Title
Convolutive Blind Source Separation Using an Iterative Least-Squares Algorithm for Non-Orthogonal Approximate Joint Diagonalization
Abstract
In this paper, we present an approach of recovering signal waveforms of speech sources from observed signals in noisy and reverberant environments. The approach is based on approximate joint diagonalization estimate to provide interference suppression of source signals and reduce echoes and distortions of separated signals. In the proposed approach, the mixing matrix is estimated by minimizing the constrained direct least-squares (LS) criterion in direct model. Exclusively under the condition where the estimated mixing matrix is not of full rank, it is replaced by a full-rank matrix. The unmixing matrix from the estimated mixing matrix is obtained by setting the frequency response of the composite mixing-unmixing filter to identity matrix. The cross-spectral density diagonal matrices of the source signals are precisely estimated by minimizing the indirect LS criterion in indirect model. These operations are fulfilled by using alternating least-squares algorithm. The correlation between the interfrequency power ratios is used to prevent a misalignment permutation of the unmixing matrix. Finally, we compare the proposed BSS with a number of conventional BSS methods in noisy and reverberant environments under both artificial and actual conditions.
Year
DOI
Venue
2015
10.1109/TASLP.2015.2485663
IEEE/ACM Trans. Audio, Speech & Language Processing
Keywords
Field
DocType
Speech processing,Microphones,Blind source separation,Noise measurement,Lagrange multipliers,Least square methods
Rank (linear algebra),Speech processing,Frequency response,Lagrange multiplier,Matrix (mathematics),Speech recognition,Identity matrix,Diagonal matrix,Blind signal separation,Mathematics
Journal
Volume
Issue
ISSN
23
12
2329-9290
Citations 
PageRank 
References 
8
0.52
30
Authors
3
Name
Order
Citations
PageRank
Shinya Saito1111.95
kunio oishi281.54
Toshihiro Furukawa35222.17