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IVA algorithms using a multivariate student’s t source prior for speech source separation in real room environments

Rafique, W, Naqvi, SM, Jackson, PJB and Chambers, JA (2015) IVA algorithms using a multivariate student’s t source prior for speech source separation in real room environments In: ICASSP 2015, Brisbane, Australia.

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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 avoid 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 original 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.

Item Type: Conference or Workshop Item (Conference Paper)
Authors :
NameEmailORCID
Rafique, WUNSPECIFIEDUNSPECIFIED
Naqvi, SMUNSPECIFIEDUNSPECIFIED
Jackson, PJBUNSPECIFIEDUNSPECIFIED
Chambers, JAUNSPECIFIEDUNSPECIFIED
Date : April 2015
Identification Number : 10.1109/ICASSP.2015.7178014
Additional Information : (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 10:57
Last Modified : 28 Mar 2017 10:57
URI: http://epubs.surrey.ac.uk/id/eprint/809393

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