Independent vector analysis with multivariate student's t ‐distribution source prior for speech separation

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.