Regularized non-negative matrix factorization with temporal dependencies for speech denoising

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Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization (2013, methodology): “Each time-frequency bin of a magnitude spectrogram ( Y kt ) is assumed to be a sum of some Poisson-distributed hidden random variables ( Z kit ). dependencies of the speech and noise signals, an l 2 -norm regularization term is added to the cost function.”
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction (2016, background): “An exception is the work in [22], where the authors use specially designed regularizers to encourage a log-normal structure on the temporal coefficients.”
Multi-objective learning and mask-based post-processing for deep neural network based speech enhancement (2017, background): “Non-negative matrix factorization (NMF) based speech enhancement [7, 8] was one notable example in which speech and noise basis models were learned separately from training speech and noise databases.”