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Source separation and other audio applications have traditionally relied on the use of short-time Fourier transforms as a front-end frequency domain representation step.
J. S. Garofolo, L. F. Lamel, J. G. F. William M Fisher, D. S. Pallett, N. L. Dahlgren, and V. Zue, “Timit acoustic phonetic continuous speech corpus,” Philadelphia, 1993
1993
Earlier work this paper cites.
C. Févotte, R. Gribonval, and E. Vincent, “Bss_eval toolbox user guide–revision 2.0,” 2005
2005
Earlier work this paper cites.
P. S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, “Deep learning for monaural speech separation,” in
2014
Earlier work this paper cites.
E. M. Grais, M. U. Sen, and H. Erdogan, “Deep neural networks for single channel source separation,” in
2014
Earlier work this paper cites.
O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,”
2014
Earlier work this paper cites.
S. Dieleman and B. Schrauwen, “End-to-end learning for music audio,” in
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in
2014
Cited alongside, same era.
M. Kim and P. Smaragdis, “Adaptive denoising autoencoders: A fine-tuning scheme to learn from test mixtures,” in
2015
Cited alongside, same era.
T. N. Sainath, R. J. Weiss, A. W. Senior, K. W. Wilson, and O. Vinyals, “Learning the speech front-end with raw waveform cldnns,” in
2015
Cited alongside, same era.
X.-L. Zhang and D. Wang, “A deep ensemble learning method for monaural speech separation,”
2016
Cited alongside, same era.
P. Smaragdis and S. Venkataramani, “A neural network alternative to non-negative audio models,” in
2017
Closest in time.
P. Chandna, M. Miron, J. Janer, and E. Gómez, “Monoaural audio source separation using deep convolutional neural networks,” in
2017
Closest in time.
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,” in
2017
Closest in time.
2017
Closest in time.
S. Venkataramani, C. Subakan, and P. Smaragdis, “Neural network alternatives to convolutive models for source separation,” in
2017
Closest in time.
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