Independent vector analysis using densities represented by chain-like overlapped cliques in graphical models for separation of convolutedly mixed signals

Independent vector analysis (IVA), a multivariate extension of independent component analysis, tackles the convolutedly mixed blind source separation (BSS) problem in a way to avoid the permutation problem by employing a multivariate source prior of the short-time Fourier transform (STFT) components. As the source prior in IVA, overall hyperspherical joint densities have been used, which imply that the dependence between the STFT components is invariant over bin difference. As a more effective source prior in the IVA framework, a dependence model is proposed that can be represented by chain-like overlaps of local cliques in graphical models. For convolutive BSS, the proposed method demonstrates consistently improved performance over using the overall hyperspherical joint density representation.