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Deep learning techniques have been used recently to tackle the audio source separation problem.
“A method of solving a convex programming problem with convergence rate o(1/sqr(k)),”
Y. Nesterov, · 1983
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“Reducing the dimensionality of data with neural networks,”
G. Hinton and R. Salakhutdinov, · 2006
Earlier work this paper cites.
“Performance measurement in blind audio source separation,”
E. Vincent, R. Gribonval, and C. Fevotte, · 2006
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A. Ozerov, P. Philippe, F. Bimbot, and R. Gribonval, · 2007
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“Extracting and composing robust features with denoising autoencoders,”
P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol, · 2008
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“Unsupervised feature learning for audio classification using convolutional deep belief networks,”
H. Lee, P. Pham, Y. Largman, and A. Y. Ng, · 2009
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“Stacked Denoising Autoencoders: learning useful representations in a deep network with a local denoising criterion,”
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, · 2010
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“Evaluation of pooling operations in convolutional architectures for object recognition,”
D. Scherer, A. Muller, and S. Behnke, · 2010
Earlier work this paper cites.
“Semi-blind speech-music separation using sparsity and continuity priors,”
H. Erdogan and E. M. Grais, · 2010
Earlier work this paper cites.
“Stacked convolutional auto-encoders for hierarchical feature extraction,”
J. Masci, U. Meier, D. Ciresan, and J. Schmidhuber, · 2011
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“Image denoising and inpainting with deep neural networks,”
J. Xie, L. Xu, and E. Chen, · 2012
Earlier work this paper cites.
“Audio-Visual speech recognition with background music using single-channel source separation,”
E. M. Grais, I. S. Topkaya, and H. Erdogan, · 2012
Earlier work this paper cites.
“Spectro-temporal post-smoothing in NMF based single-channel source separation,”
E. M. Grais and H. Erdogan, · 2012
Cited alongside, same era.
“Theano: new features and speed improvements,”
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. W. F., and Y. Bengio, · 2012
Cited alongside, same era.
“On the importance of initialization and momentum in deep learning,”
I. Sutskever, J. Martens, G. Dahl, and G. Hinton, · 2013
Cited alongside, same era.
“Singing-Voice separation from monaural recordings using deep recurrent neural networks,”
P. S. Huang, M. Kim, M. H. J., and P. Smaragdis, · 2014
Cited alongside, same era.
“Source separation using regularized NMF with MMSE estimates under GMM priors with online learning for the uncertainties,”
E. M. Grais and H. Erdogan, · 2014
Cited alongside, same era.
“A fully convolutional neural network for speech enhancement,”
S. R. Park and J. W. Lee, · 2016
Later among the works it cites.
“Music removal by convolutional denoising autoencoder in speech recognition,”
M. Zhao, D. Wang, Z. Zhang, and X. Zhang, · 2016
Later among the works it cites.
“Single channel audio source separation using deep neural network ensembles,”
E. M. Grais, G. Roma, A. J. R. Simpson, and M. D. Plumbley, · 2016
Later among the works it cites.
“Combining mask estimates for single channel audio source separation using deep neural networks,”
E. M. Grais, G. Roma, A. J. R. Simpson, and M. D Plumbley, · 2016
Later among the works it cites.
“Multichannel audio source separation with deep neural networks,”
A. A. Nugraha, A. Liutkus, and E. Vincent, · 2016
Later among the works it cites.
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M. Kim and P. Smaragdis, · 2015
Cited alongside, same era.
“The 2015 signal separation evaluation campaign,”
N. Ono, Z. Rafii, D. Kitamura, N. Ito, and A. Liutkus, · 2015
Cited alongside, same era.
“Keras, https://github.com/fchollet/keras,” 2015
F. Chollet, · 2015
Cited alongside, same era.
“Very deep convolutional neural networks for noise robust speech recognition,”
Y. Qian, M. Bi, T. Tan, and K. Yu, · 2016
Cited alongside, same era.
“SNR-Aware convolutional neural network modeling for speech enhancement,”
S. W. Fu, Y. Tsao, and X. Lu, · 2016
Cited alongside, same era.
“Convolutional recurrent neural networks for music classification,”
K. Choi, G. Fazekas, M. Sandler, and K. Cho, · 2016
Cited alongside, same era.
“A fully convolutional deep auditory model for musical chord recognition,”
F. Korzeniowski and G. Widmer, · 2016
Cited alongside, same era.
“Improving music source separation based on deep neural networks through data augmentation and network blending,”
S. Uhlich, M. Porcu, F. Giron, M. Enenkl, T. Kemp, N. Takahashi, and Y. Mitsufuji, · 2017
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“Discriminative enhancement for single channel audio source separation using deep neural networks,”
E. M. Grais, G. Roma, A. J.R. Simpson, and M. D. Plumbley, · 2017
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“Monoaural audio source separation using deep convolutional neural networks,”
P. Chandna, M. Miron, J. Janer, and E. Gomez, · 2017
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“A neural network alternative to non-negative audio models,”
P. Smaragdis and S. Venkataramani, · 2017
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“Convolutional gated recurrent neural network incorporating spatial features for audio tagging,”
Y. Xu, Q. Kong, Q. Huang, W. Wang, and M. D. Plumbley, · 2017
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“Deep convolutional neural networks for predominant instrument recognition in polyphonic music,”
Y. Han, J. Kim, and K. Lee, · 2017
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“Stacked convolutional denoising auto-encoders for feature representation,”
B. Du, W. Xiong, J. Wu, L. Zhang, L. Zhang, and D. Tao, · 2017
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