Dynamic Non-negative Models for Audio Source Separation

2018 ◽  
pp. 49-71 ◽  
Author(s):  
Paris Smaragdis ◽  
Gautham Mysore ◽  
Nasser Mohammadiha
Keyword(s):  
Source Separation ◽  
Audio Source Separation

Monoaural Audio Source Separation Using Variational Autoencoders

2018 ◽  
Author(s):  
Laxmi Pandey ◽  
Anurendra Kumar ◽  
Vinay Namboodiri
Keyword(s):  
Source Separation ◽  
Audio Source Separation

scholarly journals Multichannel Audio Source Separation With Deep Neural Networks

2016 ◽  
Vol 24 (9) ◽  
pp. 1652-1664 ◽  
Author(s):  
Aditya Arie Nugraha ◽  
Antoine Liutkus ◽  
Emmanuel Vincent
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Source Separation ◽  
Multichannel Audio ◽  
Audio Source Separation

scholarly journals Audio Source Separation using Sparse Representations

2010 ◽  
pp. 246-265 ◽  
Author(s):  
Andrew Nesbit ◽  
Maria G. Jafar ◽  
Emmanuel Vincent ◽  
Mark D. Plumbley
Keyword(s):  
Source Separation ◽  
Feedback System ◽  
Decomposition Methods ◽  
Audio Coding ◽  
Future Research ◽  
Separation Performance ◽  
Audio Source Separation ◽  
Sparse Component Analysis ◽  
Good Signal ◽  
Music Signals

The authors address the problem of audio source separation, namely, the recovery of audio signals from recordings of mixtures of those signals. The sparse component analysis framework is a powerful method for achieving this. Sparse orthogonal transforms, in which only few transform coefficients differ significantly from zero, are developed; once the signal has been transformed, energy is apportioned from each transform coefficient to each estimated source, and, finally, the signal is reconstructed using the inverse transform. The overriding aim of this chapter is to demonstrate how this framework, as exemplified here by two different decomposition methods which adapt to the signal to represent it sparsely, can be used to solve different problems in different mixing scenarios. To address the instantaneous (neither delays nor echoes) and underdetermined (more sources than mixtures) mixing model, a lapped orthogonal transform is adapted to the signal by selecting a basis from a library of predetermined bases. This method is highly related to the windowing methods used in the MPEG audio coding framework. In considering the anechoic (delays but no echoes) and determined (equal number of sources and mixtures) mixing case, a greedy adaptive transform is used based on orthogonal basis functions that are learned from the observed data, instead of being selected from a predetermined library of bases. This is found to encode the signal characteristics, by introducing a feedback system between the bases and the observed data. Experiments on mixtures of speech and music signals demonstrate that these methods give good signal approximations and separation performance, and indicate promising directions for future research.

scholarly journals Multi-Channel Audio Source Separation Using Multiple Deformed References

2015 ◽  
Vol 23 (11) ◽  
pp. 1775-1787 ◽  
Author(s):  
Nathan Souviraa-Labastie ◽  
Anaik Olivero ◽  
Emmanuel Vincent ◽  
Frederic Bimbot
Keyword(s):  
Source Separation ◽  
Audio Source Separation

Local AM/FM Parameters Estimation: Application to Sinusoidal Modeling and Blind Audio Source Separation

2018 ◽  
Vol 25 (10) ◽  
pp. 1600-1604 ◽  
Author(s):  
Dominique Fourer ◽  
Francois Auger ◽  
Geoffroy Peeters
Keyword(s):  
Source Separation ◽  
Parameters Estimation ◽  
Audio Source Separation ◽  
Sinusoidal Modeling
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