Independent Low-Rank Matrix Analysis Based on Multivariate Complex Exponential Power Distribution

Abstract

Independent low-rank matrix analysis (ILRMA), a unified method of independent vector analysis (IVA) and nonnegative matrix factorization (NMF), is a state-of-the-art blind source separation method for convolutive mixtures. Although ILRMA provides high separation performance for music signals whose spectra can be well modeled by NMF, speech spectra do not have low-rank properties, and modeling them by NMF is not appropriate. In this paper, to stably improve the separation performance of ILRMA for speech mixtures, a source spectrum model in ILRMA is generalized to explicitly model the strong higher-order correlations between neighboring frequency bins of speech signals. In addition, multivariate complex exponential power distributions, which are recognized to have high performance with IVA, are introduced as source distributions assumed in ILRMA. Experimental results show the effectiveness of the proposed method over the original ILRMA when separating speech mixtures.