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Singing-voice separation from monaural recordings using deep recurrent neural networks
Po Sen Huang
,
Minje Kim
,
Mark Hasegawa-Johnson
, Paris Smaragdis
Electrical and Computer Engineering
Coordinated Science Lab
Speech and Hearing Science
Linguistics
Beckman Institute for Advanced Science and Technology
Siebel School of Computing and Data Science
Center for Social & Behavioral Science
Research output
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peer-review
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Keyphrases
Art Performance
33%
Channel Information
33%
Deep Recurrent Neural Network
100%
Discriminative Training
33%
Interference Ratio
33%
Monaural
100%
Music Source Separation
33%
Nonlinear Operation
33%
Real-world Application
33%
Singing Voice Separation
100%
Single Channel
33%
Source Signal
33%
Training Objectives
33%
Engineering
Interference Ratio
33%
Real World Application
33%
Recurrent Neural Network
100%
Single Channel
33%
Source Separation
33%
Source Signal
33%
Computer Science
Art Performance
33%
Deep Recurrent Neural Network
100%
Information Channel
33%
Source Separation
33%
Training Objective
33%
World Application
33%
Chemical Engineering
Recurrent Neural Network
100%