Efficient Source Separation Using Bitwise Neural Networks

2018 ◽  
pp. 187-206 ◽  
Author(s):  
Minje Kim ◽  
Paris Smaragdis
Keyword(s):  
Neural Networks ◽  
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 Blind Source Separation for Convolutive Mixtures with Neural Networks

2011 ◽  
Vol 11 (1) ◽  
pp. 63-68 ◽  
Author(s):  
B. S. KIREI ◽  
M. D. TOPA ◽  
I. MURESAN ◽  
I. HOMANA ◽  
N. TOMA
Keyword(s):  
Neural Networks ◽  
Blind Source Separation ◽  
Source Separation ◽  
Convolutive Mixtures

scholarly journals Blind Nonnegative Source Separation Using Biological Neural Networks

2017 ◽  
Vol 29 (11) ◽  
pp. 2925-2954 ◽  
Author(s):  
Cengiz Pehlevan ◽  
Sreyas Mohan ◽  
Dmitri B. Chklovskii
Keyword(s):  
Neural Networks ◽  
Source Separation ◽  
Physical Nature ◽  
Similarity Matching ◽  
Matching Problem ◽  
Data Set ◽  
Learning Rules ◽  
Mixing Matrix ◽  
Online Setting ◽  
Special Case

Blind source separation—the extraction of independent sources from a mixture—is an important problem for both artificial and natural signal processing. Here, we address a special case of this problem when sources (but not the mixing matrix) are known to be nonnegative—for example, due to the physical nature of the sources. We search for the solution to this problem that can be implemented using biologically plausible neural networks. Specifically, we consider the online setting where the data set is streamed to a neural network. The novelty of our approach is that we formulate blind nonnegative source separation as a similarity matching problem and derive neural networks from the similarity matching objective. Importantly, synaptic weights in our networks are updated according to biologically plausible local learning rules.

scholarly journals Two-Stage Monaural Source Separation in Reverberant Room Environments Using Deep Neural Networks

2019 ◽  
Vol 27 (1) ◽  
pp. 125-139 ◽  
Author(s):  
Yang Sun ◽  
Wenwu Wang ◽  
Jonathon Chambers ◽  
Syed Mohsen Naqvi
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Source Separation ◽  
Two Stage
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