Independent vector analysis for convolutive blind noncircular source separation

Independent vector analysis (IVA), an extension of independent component analysis (ICA) from univariate components to multivariate components, is a method to tackle blind source separation (BSS) in frequency domain. IVA utilizes both the statistical independence among multivariate signals and the statistical inner dependency of each multivariate signal. However, so far there is no research on IVA for convolutive mixtures of noncircular sources. In this study, we focus on this problem and propose noncircular independent vector analysis (nc-IVA) algorithm, by deriving a new fixed-point algorithm that uses the information of pseudo-covariance matrix in each frequency bin. This modification provides more widely application scenarios with noncircular sources. Simulations demonstrate the effectiveness of our proposed method.

► Noncircular independent vector analysis (nc-IVA) algorithm is proposed. ► The update rule is obtained by using pseudo-covariance matrix in each frequency bin. ► The nc-IVA algorithm can alleviate permutation ambiguity and solve scaling problem. ► The nc-IVA algorithm can work well even with high noncircularity of sources. ► Simulations demonstrate the effectiveness of our proposed method.