Spectral decomposition by nonnegative matrix factorisation (NMF) has become state-of-the-
art practice in many audio signal processing tasks, such as source separation, enhancement …

This paper summarizes the 2013 community-based Signal Separation Evaluation Campaign
(SiSEC 2013). Five speech and music datasets were contributed, including two new …

We present a blind source separation algorithm named GCC-NMF that combines
unsupervised dictionary learning via non-negative matrix factorization (NMF) with spatial …

This work introduces a new framework for nonnegative matrix factorization (NMF) in
multisensor or multimodal data configurations, where taking into account the mutual …

Speech separation quality can be improved by exploiting textual information. However, this
usually requires text-to-speech alignment at phoneme level. Classical alignment methods …

A speech signal captured by a distant microphone is generally contaminated by background
noise, which severely degrades the audible quality and intelligibility of the observed signal …

The Flexible Audio Source Separation Toolbox (FASST) is a toolbox for audio source
separation that relies on a general modeling and estimation framework that is applicable to …

This chapter provides an overview of how deep learning techniques can be used for audio
signals. We first review the main DNN architectures, meta-architectures and training …

In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind speech
enhancement algorithm that combines the non-negative matrix factorization (NMF) …

Audio source separation is addressed using time–frequency filtering and conditional
adversarial networks. First, pitch tracks in the mixed audio are estimated using a multi-pitch …