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