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This paper presents a new input format, channel-wise subband input (CWS), for convolutional neural networks (CNN) based music source separation (MSS) models in the frequency domain.
Y. LeCun and Y. Bengio,
1998
Earlier work this paper cites.
N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis,”
1998
Earlier work this paper cites.
J. Taghia and M. A. Doostari, “Subband-based single-channel source separation of instantaneous audio mixtures,”
2009
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,”
2011
Earlier work this paper cites.
P. Huang, M. Kim, M. Hasegawajohnson, and P. Smaragdis, “Deep learning for monaural speech separation,”
2014
Earlier work this paper cites.
I. Moazzen and P. Agathoklis, “A general approach for filter bank design using optimization,”
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,”
2015
Earlier work this paper cites.
A. Jansson, E. Humphrey, N. Montecchio, R. Bittner, A. Kumar, and T. Weyde, “Singing voice separation with deep u-net convolutional networks,”
2017
Cited alongside, same era.
N. Takahashi and Y. Mitsufuji, “Multi-scale multi-band densenets for audio source separation,” pp. 21–25, 2017
2017
Cited alongside, same era.
L. Antoine, S. Fabianrobert, R. Zafar, K. Daichi, R. Bertrand, I. Nobutaka, O. Nobutaka, and F. Julie, “The 2016 signal separation evaluation campaign,”
2017
Cited alongside, same era.
S. Uhlich, M. Porcu, F. Giron, M. Enenkl, T. Kemp, N. Takahashi, and Y. Mitsufuji, “Improving music source separation based on deep neural networks through data augmentation and network blending,” pp. 261–265, 2017
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. V. Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,”
2017
2019
Later among the works it cites.
2019
Later among the works it cites.
Z. Rafii, A. Liutkus, F.-R. Stöter, S. I. Mimilakis, and R. Bittner, “Musdb18-hq - an uncompressed version of musdb18,” 2019
2019
Later among the works it cites.
C. Yu, H. Lu, N. Hu, M. Yu, C. Weng, K. Xu, P. Liu, D. Tuo, S. Kang, G. Lei
2019
Later among the works it cites.
R. Hennequin, A. Khlif, F. Voituret, and M. Moussalam, “Spleeter: A fast and state-of-the art music source separation tool with pre-trained models,” 2019
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Cited alongside, same era.
E. Cano, D. FitzGerald, A. Liutkus, M. D. Plumbley, and F.-R. Stöter, “Musical source separation: An introduction,”
2018
Cited alongside, same era.
N. Takahashi, N. Goswami, and Y. Mitsufuji, “Mmdenselstm: An efficient combination of convolutional and recurrent neural networks for audio source separation,” pp. 106–110, 2018
2018
Cited alongside, same era.
F.-R. Stöter, A. Liutkus, and N. Ito, “The 2018 signal separation evaluation campaign,” pp. 293–305, 2018
2018
Cited alongside, same era.
E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, and M. E. Davies, “Performance measurement in blind audio source separation,”
Cited in the paper.
2019
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2020
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2020
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P. Chandna, M. Blaauw, J. Bonada, and E. Gómez, “Content based singing voice extraction from a musical mixture,”
2020
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