In recent years, symmetric non-negative matrix factorization (SNMF), a variant of non-
negative matrix factorization (NMF), has emerged as a promising tool for data analysis. This …

This article addresses the problem of multichannel audio source separation. We propose a
framework where deep neural networks (DNNs) are used to model the source spectra and …

Deep neural networks have become an indispensable technique for audio source
separation (SS). It was recently reported that a variant of CNN architecture called MM …

This paper deals with the problem of audio source separation. To handle the complex and ill-
posed nature of the problems of audio source separation, the current state-of-the-art …

This paper proposes several improvements for music separation with deep neural networks
(DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using …

Background and objective: Valvular heart disease (VHD) is associated with elevated
mortality rates. Although transthoracic echocardiography (TTE) is the gold standard …

In this paper, statistical-model generalizations of independent low-rank matrix analysis
(ILRMA) are proposed for achieving high-quality blind source separation (BSS). BSS is a …

Recently, nonnegative matrix factorization (NMF) has achieved a great success in
hyperspectral image (HSI) unmixing tasks. However, existing NMF based unmixing methods …

Singing voice separation based on deep learning relies on the usage of time-frequency
masking. In many cases the masking process is not a learnable function or is not …

This paper presents a robust variant of nonnegative matrix factorization (NMF) based on
complex Student's t distributions (t-NMF) for source separation of single-channel audio …