The Generalization of Non-Negative Matrix Factorization Based on Algorithmic Stability

Non-stationary Noise Estimation Based on Non-negative Matrix Factorization

In this paper, we apply a non-negative matrix factorization (NMF) technique to propose a method of estimating noise occurring in non-stationary environments. In the proposed method, the basis matrix of the target noise is first obtained via NMF training. The noise basis is then applied to estimate an activation matrix of the target noise from the noisy signal. The proposed method is finally app...

Non-negative matrix factorization

Non-negative Matrix Factorization on Kernels

In this paper, we extend the original non-negative matrix factorization (NMF) to kernel NMF (KNMF). The advantages of KNMF over NMF are: 1) it could extract more useful features hidden in the original data through some kernel-induced nonlinear mappings; 2) it can deal with data where only relationships (similarities or dissimilarities) between objects are known; 3) it can process data with nega...

Non-negative Matrix Factorization on GPU

Today, the need of large data collection processing increase. Such type of data can has very large dimension and hidden relationships. Analyzing this type of data leads to many errors and noise, therefore, dimension reduction techniques are applied. Many techniques of reduction were developed, e.g. SVD, SDD, PCA, ICA and NMF. Non-negative matrix factorization (NMF) has main advantage in process...

Multimodal voice conversion based on non-negative matrix factorization

A multimodal voice conversion (VC) method for noisy environments is proposed. In our previous non-negative matrix factorization (NMF)-based VC method, source and target exemplars are extracted from parallel training data, in which the same texts are uttered by the source and target speakers. The input source signal is then decomposed into source exemplars, noise exemplars, and their weights. Th...