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Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement.
“All-pole modeling of degraded speech,”
J. Lim and A. Oppenheim, · 1978
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“Suppression of acoustic noise in speech using spectral subtraction,”
S. Boll, · 1979
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“Speech enhancement using a minimum mean-square error log-spectral amplitude estimator,”
Y. Ephraim and D. Malah, · 1985
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“PEAQ - the ITU standard for objective measurement of perceived audio quality,”
T. Thiede, W. C. Treurniet, R. Bitto, C. Schmidmer, T. Sporer, J. G. Beerends, and C. Colomes, · 2000
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“Spectral subtraction-based speech enhancementfor cochlear implant patients in background noise,”
L.-P. Yang and Q.-J. Fu, · 2005
Earlier work this paper cites.
“Unbiased MMSE-based noise power estimation with low complexity and low tracking delay,”
T. Gerkmann and R. C. Hendriks, · 2011
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“An algorithm for intelligibility prediction of time-frequency weighted noisy speech,”
C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, · 2011
Earlier work this paper cites.
“Lecture 6.5 - RMSprop: divide the gradient by a running average of its recent magnitude,”
T. Tieleman and G. Hinton, · 2012
Earlier work this paper cites.
Speech Enhancement: Theory and Practice
P. C. Loizou, · 2013
Earlier work this paper cites.
“The voice bank corpus: design, collection and data analysis of a large regional accent speech database,”
C. Veaux, J. Yamagishi, and S. King, · 2013
Earlier work this paper cites.
“The diverse environments multi-channel acoustic noise database: A database of multichannel environmental noise recordings,”
J. Thiemann, N. Ito, and E. Vincent, · 2013
Earlier work this paper cites.
“Generative adversarial nets,”
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, · 2014
Earlier work this paper cites.
“Speech enhancement with lstm recurrent neural networks and its application to noise-robust asr,”
F. Weninger, H. Erdogan, S. Watanabe, E. Vincent, J. Le Roux, J. R. Hershey, and B. Schuller, · 2015
Cited alongside, same era.
“A regression approach to speech enhancement based on deep neural networks,”
Y. Xu, J. Du, L.-R. Dai, and C.-H. Lee, · 2015
Cited alongside, same era.
“Phase sensitive and recognition-boosted speech separation using deep recurrent neural networks,”
H. Erdogan, J. R. Hershey, S. Watanabe, and J. Le Roux, · 2015
Cited alongside, same era.
“Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,”
K. He, X. Zhang, S. Ren, and J. Sun, · 2015
Cited alongside, same era.
“Speech enhancement in multiple-noise conditions using deep neural networks,”
A. Kumar and D. Florencio, · 2016
Cited alongside, same era.
“SEGAN: Speech enhancement generative adversarial network,”
S. Pascual, A. Bonafonte, and J. Serrà, · 2017
Later among the works it cites.
“Adversarial training for data-driven speech enhancement without parallel corpus,”
T. Higuchi, K. Kinoshita, M. Delcroix, and T. Nakatani, · 2017
Later among the works it cites.
“Least squares generative adversarial networks,”
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang, and S. P. Smolley, · 2017
Later among the works it cites.
“Image-to-image translation with conditional adversarial networks,”
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, · 2017
Later among the works it cites.
“Improved wasserstein conditional generative adversarial network speech enhancement,”
S. Qin and T. Jiang, · 2018
Later among the works it cites.
“A conditional generative model for speech enhancement,”
Z. X. Li, L. R. Dai, Y. Song, and I. McLoughlin, · 2018
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D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, · 2016
Cited alongside, same era.
“Unsupervised representation learning with deep convolutional generative adversarial networks,”
A. Radford, L. Metz, and S. Chintala, · 2016
Cited alongside, same era.
“Improved techniques for training GANs,”
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, · 2016
Cited alongside, same era.
“Investigating RNN-based speech enhancement methods for noise-robust text-to-speech,”
C. Valentini-Botinhao, X. Wang, S. Takaki, and J. Yamagishi, · 2016
Cited alongside, same era.
“Tensorflow: Large-scale machine learning on heterogeneous distributed systems,”
M. Abadi et al. , · 2016
Cited alongside, same era.
“A fully convolutional neural network for speech enhancement,”
S. R. Park and J. Lee, · 2017
Cited alongside, same era.
Later among the works it cites.
“Exploring speech enhancement with generative adversarial networks for robust speech recognition,”
B. Li C. Donahue and R. Prabhavalkar, · 2018
Later among the works it cites.
“Compressed sensing mri reconstructionusing a generative adversarial network with a cyclic loss,”
T. M. Quan, T. Nguyen-Duc, and W.-K. Jeong, · 2018
Later among the works it cites.
“Convolutional neural network-based speech enhancement for cochlear implant recipients,”
N. Mamun, S. Khorram, and J. H. L. Hansen, · 2019
Later among the works it cites.
“Towards generalized speech enhancement with generative adversarial networks,”
S. Pascual, J. Serrà, and A. Bonafonte, · 2019
Later among the works it cites.
“Speech enhancement using a two-stage network for an efficient boosting strategy,”
J. Kim and M. Hahn, · 2019
Later among the works it cites.