We describe nonnegative matrix factorisation (NMF) with a Kullback‐Leibler (KL) error
measure in a statistical framework, with a hierarchical generative model consisting of an …

Source separation consists of separating a signal into additive components. It is a topic of
considerable interest with many applications that has gathered much attention recently …

In the recent years, many studies have focused on the single-sensor separation of
independent waveforms using so-called soft-masking strategies, where the short term …

Gaussian process (GP) models are very popular for machine learning and regression and
they are widely used to account for spatial or temporal relationships between multivariate …

Nonnegative matrix factorization (NMF) is an effective and popular low-rank model for
nonnegative data. It enjoys a rich background, both from an optimization and probabilistic …

We address the issue of underdetermined source separation in a particular informed
configuration where both the sources and the mixtures are known during a so-called …

The separation of the lead vocals from the background accompaniment in audio recordings
is a challenging task. Recently, an efficient method called REPET (REpeating Pattern …

Recently, Kernel Additive Modelling (KAM) was proposed as a unified framework to achieve
multichannel audio source separation. Its main feature is to use kernel models for locally …

This paper proposes a new view of time-frequency analysis framed in terms of probabilistic
inference. Natural signals are assumed to be formed by the superposition of distinct time …

Dimension reduction techniques such as Nonnegative Tensor Factorization are now
classical for both source separation and estimation of multiple fundamental frequencies in …