Title
A Region-Growing Permutation Alignment Approach in Frequency-Domain Blind Source Separation of Speech Mixtures
Abstract
The convolutive blind source separation (BSS) problem can be solved efficiently in the frequency domain, where instantaneous BSS is performed separately in each frequency bin. However, the permutation ambiguity in each frequency bin should be resolved so that the separated frequency components from the same source are grouped together. To solve the permutation problem, this paper presents a new alignment method based on an inter-frequency dependence measure: the powers of separated signals. Bin-wise permutation alignment is applied first across all frequency bins, using the correlation of separated signal powers; then the full frequency band is partitioned into small regions based on the bin-wise permutation alignment result. Finally, region-wise permutation alignment is performed in a region-growing manner. The region-wise permutation correction scheme minimizes the spreading of the misalignment at isolated frequency bins to others, hence to improve permutation alignment. Experiment results in simulated and real environments verify the effectiveness of the proposed method. Analysis demonstrates that the proposed frequency-domain BSS method is computationally efficient.
Year
DOI
Venue
2011
10.1109/TASL.2010.2052244
IEEE Transactions on Audio, Speech & Language Processing
Keywords
Field
DocType
frequency-domain blind source separation,frequency domain,permutation alignment,region-growing permutation alignment approach,speech mixtures,region-wise permutation alignment,frequency bin,bin-wise permutation alignment result,permutation problem,permutation ambiguity,bin-wise permutation alignment,isolated frequency bin,full frequency band,speech,blind source separation,time frequency analysis,speech processing,region growing,acoustical engineering,frequency domain analysis
Frequency domain,Signal processing,Pattern recognition,Bin,Frequency band,Computer science,Permutation,Speech recognition,Artificial intelligence,Time–frequency analysis,Blind signal separation,Source separation
Journal
Volume
Issue
ISSN
19
3
1558-7916
Citations 
PageRank 
References 
21
0.77
18
Authors
3
Name
Order
Citations
PageRank
Lin Wang117715.50
Heping Ding2677.26
Fuliang Yin342650.82