This paper addresses the determined blind source separation problem and proposes a new effective method unifying independent vector analysis (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between sources in a mixture signal, and an efficient optimization scheme has been proposed for IVA. However, since the source model in IVA is based on a spherical multivariate distribution, IVA cannot utilize specific spectral structures such as the harmonic structures of pitched instrumental sounds. To solve this problem, we introduce NMF decomposition as the source model in IVA to capture the spectral structures. The formulation of the proposed method is derived from conventional multichannel NMF (MNMF), which reveals the relationship between MNMF and IVA. The proposed method can be optimized by the update rules of IVA and single-channel NMF. Experimental results show the efficacy of the proposed method compared with IVA and MNMF in terms of separation accuracy and convergence speed.Index Terms-Blind source separation, determined, independent vector analysis, nonnegative matrix factorization.

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International audienceIn this paper, we report the results of the 2016 community-based Signal Separation Evaluation Campaign (SiSEC 2016). This edition comprises four tasks. Three focus on the separation of speech and music audio recordings, while one concerns biomedical signals. We summarize these tasks and the performance of the submitted systems, as well as provide a small discussion concerning future trends of SiSEC

This paper presents a new sparse representation for acoustic signals which is based on a mixing model defined in the complex-spectrum domain (where additivity holds), and allows us to extract recurrent patterns of magnitude spectra that underlie observed complex spectra and the phase estimates of constituent signals. An efficient iterative algorithm is derived, which reduces to the multiplicative update algorithm for non-negative matrix factorization developed by Lee under a particular condition.

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