Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization

@article{Kitamura2016DeterminedBS,
  title={Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization},
  author={Daichi Kitamura and Nobutaka Ono and Hiroshi Sawada and Hirokazu Kameoka and Hiroshi Saruwatari},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  year={2016},
  volume={24},
  pages={1626-1641}
}
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… CONTINUE READING
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