Joint speaker separation and recognition using non-negative matrix deconvolution with adaptive dictionary
S Drgas, T Virtanen - Computer Speech & Language, 2021 - Elsevier
In this article, we propose a new method for joint cochannel speaker separation and
recognition called adaptive-dictionary non-negative matrix deconvolution (DANMD). This …
recognition called adaptive-dictionary non-negative matrix deconvolution (DANMD). This …
Joint sound source separation and speaker recognition
J Zegers - arXiv preprint arXiv:1604.08852, 2016 - arxiv.org
Non-negative Matrix Factorization (NMF) has already been applied to learn speaker
characterizations from single or non-simultaneous speech for speaker recognition …
characterizations from single or non-simultaneous speech for speaker recognition …
Single-Channel speech separation based on non-negative matrix factorization and factorial conditional random field
A new Non-negative matrix factorization (NMF) based algorithm is proposed for single-
channel speech separation with a prior known speakers, which aims to better model the …
channel speech separation with a prior known speakers, which aims to better model the …
Single‐Channel Speech Separation Based on Non‐negative Matrix Factorization and Factorial Conditional Random Field
A new Non‐negative matrix factorization (NMF) based algorithm is proposed for single‐
channel speech separation with a prior known speakers, which aims to better model the …
channel speech separation with a prior known speakers, which aims to better model the …
[PDF][PDF] Speech separation using non-negative features and sparse non-negative matrix factorization
MN Schmidt - Interspeech, 2007 - Citeseer
This paper describes a method for separating two speakers in a single channel recording.
The separation is performed in a low dimensional feature space optimized to represent …
The separation is performed in a low dimensional feature space optimized to represent …
Single-Channel Multispeaker Separation with Variational Autoencoder Spectrogram Model
This paper deals with single-channel speaker-dependent speech separation. While
discriminative approaches using deep neural networks (DNNs) have recently proved …
discriminative approaches using deep neural networks (DNNs) have recently proved …
Discriminative non-negative matrix factorization for single-channel speech separation
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 …
channel speech separation. In this paper, we propose a new method of discriminative …
Universal speech models for speaker independent single channel source separation
Supervised and semi-supervised source separation algorithms based on non-negative
matrix factorization have been shown to be quite effective. However, they require isolated …
matrix factorization have been shown to be quite effective. However, they require isolated …
Convolutive speech bases and their application to supervised speech separation
P Smaragdis - IEEE Transactions on Audio, Speech, and …, 2006 - ieeexplore.ieee.org
In this paper, we present a convolutive basis decomposition method and its application on
simultaneous speakers separation from monophonic recordings. The model we propose is a …
simultaneous speakers separation from monophonic recordings. The model we propose is a …
Monaural speaker segregation using group delay spectral matrix factorization
K Nathwani, A Kumar, RM Hegde - 2014 Twentieth National …, 2014 - ieeexplore.ieee.org
Non-negative matrix factorization (NMF) methods have been widely used in single channel
speaker separation. NMF methods use the magnitude of the Fourier transform for training …
speaker separation. NMF methods use the magnitude of the Fourier transform for training …