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State-of-the-art singing voice separation is based on deep learning making use of CNN structures with skip connections (like U-net model, Wave-U-Net model, or MSDENSELSTM).
“Improved phase vocoder time-scale modification of audio,”
J. Laroche and M. Dolson, · 1999
Earlier work this paper cites.
“A new approach to transient processing in the phase vocoder,”
A. Röbel, · 2003
Earlier work this paper cites.
“Sound analysis and processing with AudioSculpt 2,”
N. Bogaards, A. Röbel, and X. Rodet, · 2004
Earlier work this paper cites.
“Separation of vocals from polyphonic audio recordings,”
S. Vembu and S. Baumann, · 2005
Earlier work this paper cites.
“Efficient spectral envelope estimation and its application to pitch shifting and envelope preservation,”
A. Röbel and X. Rodet, · 2005
Earlier work this paper cites.
“Performance measurement in blind audio source separation,”
Emmanuel Vincent, Rémi Gribonval, and Cédric Févotte, · 2006
Earlier work this paper cites.
SWIPE: A sawtooth waveform inspired pitch estimator for speech and music
Arturo Camacho, · 2007
Earlier work this paper cites.
“On cepstral and all-pole based spectral envelope modeling with unknown model order,”
A. Röbel, F. Villavicencio, and X. Rodet, · 2007
Earlier work this paper cites.
“Source/filter model for unsupervised main melody extraction from polyphonic audio signals,”
J. L. Durrieu, G. Richard, B. David, and C. Fevotte, · 2010
Cited alongside, same era.
“Shape-invariant speech transformation with the phase vocoder,”
A. Röbel, · 2010
Cited alongside, same era.
“Robust principal component analysis?,”
Emmanuel J. Candès, Xiaodong Li, Yi Ma, and John Wright, · 2011
Cited alongside, same era.
“A simple music/voice separation method based on the extraction of the repeating musical structure,”
Z. Rafii and B. Pardo, · 2011
Cited alongside, same era.
“Adam: A method for stochastic optimization.,”
Diederik P. Kingma and Jimmy Ba, · 2014
Cited alongside, same era.
“Medleydb: A multitrack dataset for annotation-intensive mir research,”
Rachel Bittner, Justin Salamon, Mike Tierney, Matthias Mauch, Chris Cannam, and Juan Bello, · 2014
“A software framework for musical data augmentation,”
Brian McFee, Eric J. Humphrey, and Juan Pablo Bello, · 2015
Later among the works it cites.
“Monoaural audio source separation using deep convolutional neural networks,”
P. Chandna, M. Miron, J. Janer, and E. Gómez, · 2017
Later among the works it cites.
“Singing voice separation with deep u-net convolutional networks,”
A. Jansson, E. J. Humphrey, N. Montecchio, R. M. Bittner, A. Kumar, and T. Weyde, · 2017
Later among the works it cites.
“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
Later among the works it cites.
“The musdb18 corpus for music separation,” dec 2017
Zafar Rafii, Antoine Liutkus, Fabian-Robert Stöter, Stylianos Ioannis Mimilakis, and Rachel Bittner, · 2017
Later among the works it cites.
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Cited alongside, same era.
“U-net: Convolutional networks for biomedical image segmentation,”
O. Ronneberger, P.Fischer, and T. Brox, · 2015
Cited alongside, same era.
“Exploring Data Augmentation for Improved Singing Voice Detection with Neural Networks,”
Jan Schlüter and Thomas Grill, · 2015
Cited alongside, same era.
“The 2018 signal separation evaluation campaign,”
F.-R. St”oter, A. Liutkus, and N. Ito, · 2018
Later among the works it cites.
“MMDENSELSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation,”
Naoya Takahashi, Nabarun Goswami, and Yuki Mitsufuji, · 2018
Later among the works it cites.
“Wave-u-net: A multi-scale neural network for end-to-end audio source separation,”
D. Stoller, S. Ewert, and S. Dixon, · 2018
Later among the works it cites.