Independent Vector Analysis with Frequency Range Division and Prior Switching

A novel source model is developed to improve the separation performance of independent vector analysis (IVA) for speech mixtures. The source model of IVA generally assumes the same amount of statistical dependency on each pair of frequency bins, which is not effective for speech signals with strong correlations among neighboring frequency bins. In the proposed model, the set of all frequency bins is divided into frequency bands, and the statistical dependency is assumed only within each band to better represent speech signals. In addition, each source prior is switched depending on the source states, active or inactive, since intermittent silent periods have totally different priors from those of speech periods. The optimization of the model is based on an EM algorithm, in which the IVA filters, states of sources, and permutation alignments between each pair of bands are jointly optimized. The experimental results show the effectiveness of the proposed model.