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Music source separation involves a large input field to model a long-term dependence of an audio signal.
“Conv-TasNet: Surpassing ideal time–frequency magnitude masking for speech separation,”
Y. Luo and N. Mesgarani, · 2010
Earlier work this paper cites.
“Multichannel music separation with deep neural networks,”
A. A. Nugraha, A. Liutkus, and E. Vincent, · 2015
Earlier work this paper cites.
“Deep neural network based instrument extraction from music,”
S. Uhlich, F. Giron, and Y. Mitsufuji, · 2015
Earlier work this paper cites.
“Going deeper with convolutions,”
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, · 2015
Earlier work this paper cites.
“Wavenet: A generative model for raw audio,”
A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, · 2016
Earlier work this paper cites.
“Inception v4, inception-resnet and the impact of residual connections on learning,”
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, · 2016
Earlier work this paper cites.
“Improving music source separation based on deep networks through data augmentation and network blending,”
S. Uhlich, M. Porcu, F. Giron, M. Enenkl, T. Kemp, N. Takahashi, and Y. Mitsufuji, · 2017
Earlier work this paper cites.
“Multi-scale Multi-band DenseNets for Audio Source Separation,”
N. Takahashi and Y. Mitsufuji, · 2017
Earlier work this paper cites.
“Densely connected convolutional networks,”
G. Huang, Z. Liu, and L. van der Maaten, · 2017
Cited alongside, same era.
“Wave-u-net: A multi-scale neural network for end-to-end audio source separation,”
D. Stoller, S. Ewert, and S. Dixon, · 2018
Cited alongside, same era.
“Phasenet: Discretized phase modeling with deep neural networks for audio source separation,”
N. Takahashi, P. Agrawal, N. Goswami, and Y. Mitsufuji, · 2018
Cited alongside, same era.
“MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation,”
N. Takahashi, N. Goswami, and Y. Mitsufuji, · 2018
Cited alongside, same era.
“The 2018 signal separation evaluation campaign,”
A. Liutkus, F.-R. Stöter, and N. Ito, · 2018
Cited alongside, same era.
“Listening to the world improves speech command recognition,”
“Dilated convolution with dilated GRU for music source separation,”
J.-Y. Liu and Y.-H. Yang, · 2019
Later among the works it cites.
“Recurrent dilated densenets for a time-series segmentation task,”
A. Fuchs, R. Priewald, and F. Pernkopf, · 2019
Later among the works it cites.
“Open-unmix - a reference implementation for music source separation,”
F.-R. Stöter, S. Uhlich, A. Liutkus, and Y. Mitsufuji, · 2019
Later among the works it cites.
“Meta-learning extractors for music source separation,”
D. Samuel, A. Ganeshan, and J. Naradowsky, · 2020
Closest in time.
“Voice separation with an unknown number of multiple speakers,”
E. Nachmani, Y. Adi, and L. Wolf, · 2020
Closest in time.
“Spleeter: a fast and efficient music source separation tool with pre-trained models,”
R. Hennequin, A. Khlif, F. Voituret, and M. Moussallam, · 2020
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B. McMahan and D. Rao, · 2018
Cited alongside, same era.
“Music source separation in the waveform domain,”
A. Défossez, N. Usunier, L. Bottou, and F. Bach, · 2019
Cited alongside, same era.
“Spleeter: A fast and state-of-the art music source separation tool with pre-trained models,” Late-Breaking/Demo ISMIR 2019, November 2019,
R. Hennequin, A. Khlif, F. Voituret, and M. Moussallam, · 2019
Cited alongside, same era.
Closest in time.
“A frugal approach to music source separation,”
E. Pierson Lancaster and N. Souviraà-Labastie, · 2020
Closest in time.