Non-negative Matrix Factorization (NMF) has already been applied to learn speaker
characterizations from single or non-simultaneous speech for speaker recognition …

Non-negative matrix factorization (NMF) has emerged as a promising approach for single-
channel speech separation. In this paper, we propose a new method of discriminative …

Regarding the non-negativity property of the magnitude spectrogram of speech signals,
nonnegative matrix factorization (NMF) has obtained promising performance for speech …

We describe and evaluate our toolkit openBliSSART (open-source Blind Source Separation
for Audio Recognition Tasks), which is the C++ framework and toolbox that we have …

Although the task seems trivial for human listeners, research in automating source
separation still lags far behind human performance and is especially difficult for single …

Supervised and semi-supervised source separation algorithms based on non-negative
matrix factorization have been shown to be quite effective. However, they require isolated …

We are surrounded by sounds. Such a noisy environment makes it difficult to hear desired
speech and to converse comfortably. This makes it important to be able to separate and …

Non-negative matrix factorization (NMF) has been widely used for challenging single-
channel audio source separation tasks. However, inference in NMF-based models relies on …

This paper deals with audio source separation using supervised non-negative matrix
factorization (NMF). We propose a prior model based on mixtures of Gamma distributions for …

This paper presents supervised feature learning approaches for speaker identification that
rely on nonnegative matrix factorisation. Recent studies have shown that group nonnegative …