Open AccessIndependent vector analysis with multivariate student's t ‐distribution source prior for speech separation
Author(s) -
Liang Y.,
Chen G.,
Naqvi S.M.R.,
Chambers J.A.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2013.1999
Subject(s) - multivariate statistics , dependency (uml) , multivariate t distribution , blind signal separation , multivariate analysis , computer science , permutation (music) , multivariate normal distribution , source separation , copula (linguistics) , joint probability distribution , frequency domain , speech recognition , multivariate analysis of variance , speech processing , gaussian , pattern recognition (psychology) , algorithm , artificial intelligence , mathematics , statistics , machine learning , econometrics , computer network , channel (broadcasting) , physics , quantum mechanics , acoustics , computer vision
The independent vector analysis algorithm can theoretically avoid the permutation problem in frequency domain blind source separation by using a multivariate source prior to retain the dependency between different frequency bins of each source. A super‐Gaussian multivariate Student's t ‐distribution is adopted as the source prior to model the spectrum of speech signals and to mitigate imprecise variance knowledge as is commonplace in non‐stationary signal processing. Moreover, the new multivariate source prior can be interpreted as a joint distribution constructed by a t ‐copula, which can describe the nonlinear inter‐frequency dependency. Experimental results using 50 speech mixtures formed from the TIMIT database confirm the advantages of the proposed algorithm.