Fetching the paper…
Reading the bibliography…
We study the ability of Wasserstein Generative Adversarial Network (WGAN) to generate missing audio content which is, in context, (statistically similar) to the sound and the neighboring borders.
D. Goodman, G. Lockhart, O. Wasem, and W.-C. Wong, “Waveform substitution techniques for recovering missing speech segments in packed voice communications,” IEEE Transactions on Acoustics, Speech and Signal Processing
1986
Earlier work this paper cites.
W. Etter, “Restoration of a discrete-time signal segment by interpolation based on the left-sided and right-sided autoregressive parameters,” IEEE Transactions on Signal Processing
1996
Earlier work this paper cites.
I. Kauppinen, J. Kauppinen, and P. Saarinen, “A method for long extrapolation of audio signals,” Journal of the Audio Engineering Society
2001
Earlier work this paper cites.
I. Recommendation, “1387: Method for objective measurements of perceived audio quality,” International Telecommunication Union, Geneva, Switzerland
2001
Earlier work this paper cites.
M. Lagrange, S. Marchand, and J.-B. Rault, “Long interpolation of audio signals using linear prediction in sinusoidal modeling,” Audio Eng. Soc
2005
Earlier work this paper cites.
A. Lukin and J. Todd, “Parametric interpolation of gaps in audio signals,” Audio Engineering Society Convention
2008
Earlier work this paper cites.
A. Adler, V. Emiya, M. Jafari, M. Elad, R. Gribonval, and M. Plumbley, “Audio inpainting,” IEEE Transactions on Audio, Speech and Language Processing
2012
Earlier work this paper cites.
K. Siedenburg, M. Dörfler, and M. Kowalski, “Audio inpainting with social sparsity,” SPARS Signal Processing with Adaptive Sparse Structured Representations
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems
2014
Cited alongside, same era.
Y. Bahat, Y. Schechner, and M. Elad, “Self-content-based audio inpainting,” Signal Processing
2015
Cited alongside, same era.
A. Van Den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. W. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio.,” in SSW
2016
Cited alongside, same era.
T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li, “Mode regularized generative adversarial networks,” International Conference on Learning Representations
2017
Cited alongside, same era.
A. Srivastava, L. Valkov, C. Russell, M. U. Gutmann, and C. Sutton, “VEE-GAN: Reducing mode collapse in GANs using implicit variational learning,” Advances In Neutral Information Processing Systems
2017
Later among the works it cites.
N. Perraudin, “Audio inpainting with generative adversarial network,” https://github.com/nperraud/CodeGAN
2017
Later among the works it cites.
N. Perraudin, N. Holighaus, P. Majdak, and P. Balazs, “Inpainting of long audio segments with similarity graphs,” IEEE/ACM Transactions on Audio, Speech, and Language Processing
2018
Later among the works it cites.
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wassterstein GAN,” arXiv:1701.07875v3
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Later among the works it cites.
2019
Later among the works it cites.