Paper Info
Title
Iva Algorithms Using A Multivariate Student'S T Source Prior For Speech Source Separation In Real Room Environments
Abstract
The independent vector analysis (IVA) algorithm employs a multivariate source prior to retain the dependency between different frequency bins of each source and thereby avoids the permutation problem that is inherent to blind source separation (BSS). In this paper, a multivariate Student's t distribution is adopted as the source prior, which because of its heavy tail nature can better model the large amplitude information in the frequency bins. Therefore it can improve the separation performance and the convergence speed of the IVA and fast version of the IVA (FastIVA) algorithms as compared with the IVA algorithm based on another multivariate super Gaussian source prior. Separation performance with real binaural room impulse responses (BRIRs) is evaluated by detailed simulation studies when using the different source priors, and the experimental results confirm that the IVA and the FastIVA with the proposed multivariate Student's t source prior can consistently achieve improved and faster separation performance.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP)
Fast fixed point independent vector analysis, multivariate Student's t distribution, binaural room impulse responses, source separation
Field
DocType
ISSN
Speech processing,Algorithm design,Pattern recognition,Multivariate statistics,Computer science,Algorithm,Gaussian,Artificial intelligence,Heavy-tailed distribution,Prior probability,Blind signal separation,Source separation
Conference
1520-6149
Citations 
PageRank 
References 
2
0.38
14
Authors
4
Name
Order
Citations
PageRank
Waqas Rafique131.77
Syed Mohsen Naqvi241755.49
Philip J. B. Jackson317622.45
Jonathon Chambers486882.37