A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization

@article{Degleris2020APC,
  title={A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization},
  author={Anthony Degleris and N. Gillis},
  journal={IEEE Transactions on Signal Processing},
  year={2020},
  volume={68},
  pages={2499-2512}
}
In this paper, we propose a provably correct algorithm for convolutive nonnegative matrix factorization (CNMF) under separability assumptions. CNMF is a convolutive variant of nonnegative matrix factorization (NMF), which functions as an NMF with additional sequential structure. This model is useful in a number of applications, such as audio source separation and neural sequence identification. While a number of heuristic algorithms have been proposed to solve CNMF, to the best of our knowledge… Expand
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