Bilateral Denoising Diffusion Models

R. Kubichek, “Mel-cepstral distance measure for objective speech quality assessment,” in Proceedings of IEEE Pacific Rim Conference on Communications Computers and Signal Processing , vol. 1. IEEE, 1993, pp. 125–128

1993

Earlier work this paper cites.

A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,” in 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221) , vol. 2. IEEE, 2001, pp. 749–752

2001

Earlier work this paper cites.

G. E. Hinton, “Training products of experts by minimizing contrastive divergence. neural computation,” Neural computation , pp. 14(8):1771–1800, 2002

2002

Earlier work this paper cites.

M. A. Carreira-Perpinan and G. E. Hinton, “On contrastive divergence learning,” AISTATS , pp. 33–40, 2005

2005

Earlier work this paper cites.

A. Hyvarinen and P. Dayan, “Estimation of non-normalized statistical models by score matching,” Journal of Machine Learning Research , p. 6(4), 2005

2005

Earlier work this paper cites.

A. Krizhevsky, “Learning multiple layers of features from tiny images,” in Computer Science , 2009

2009

Earlier work this paper cites.

C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, “A short-time objective intelligibility measure for time-frequency weighted noisy speech,” in 2010 IEEE international conference on acoustics, speech and signal processing . IEEE, 2010, pp. 4214–4217

2010

Earlier work this paper cites.

P. Vincent, “A connection between score matching and denoising autoencoders,” Neural Computation , p. 23(7):1661–1674, 2011

2011

Earlier work this paper cites.

F. Protasio Ribeiro, D. Florencio, C. Zhang, and M. Seltzer, “CROWDMOS: An approach for crowdsourcing mean opinion score studies,” in ICASSP . IEEE, 2011, edition: ICASSP

2011

Earlier work this paper cites.

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013

Original

2013

Earlier work this paper cites.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” arXiv:1406.2661 , 2014

Original

2014

Earlier work this paper cites.

D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” ICML , pp. 1278–1286, 2014

2014

Earlier work this paper cites.

J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” In International Conference on Machine Learning , pp. 2256–2265, 2015

2015

Earlier work this paper cites.

A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio,” Proc. 9th ISCA Speech Synthesis Workshop , pp. 125–125, 2016

2016

Earlier work this paper cites.

L. Dinh, J. Sohl-Dickstein, and S. Bengio, “Density estimation using real nvp,” arXiv preprint arXiv:1605.08803 , 2016

Original

2016

Earlier work this paper cites.

K. Ito and L. Johnson, “The lj speech dataset,” https://keithito.com/LJ-Speech-Dataset/ , 2017

2017

Earlier work this paper cites.

D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” NeurIPS , pp. 10 215–10 224, 2018

2018

Cited alongside, same era.

T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” NeurIPs , pp. 6571–6583, 2018

2018

Cited alongside, same era.

N. Kalchbrenner, E. Elsen, K. Simonyan, S. Noury, N. Casagrande, E. Lockhart, F. Stimberg, A. Oord, S. Dieleman, and K. Kavukcuoglu, “Efficient neural audio synthesis,” In International Conference on Machine Learning , pp. 2410–2419, 2018

2018

Cited alongside, same era.

J. Donahue and K. Simonyan, “Large scale adversarial representation learning,” arXiv:1907.02544 , 2019

Original

2019

Cited alongside, same era.

A. Razavi, A. van den Oord, and O. Vinyals, “Generating diverse high-fidelity images with vq-vae-2,” arXiv preprint arXiv:1906.00446 , 2019

R. Yamamoto, E. Song, and J.-M. K. Parallel, “Wavegan: A fast waveform gen- eration model based on generative adversarial networks with multi-resolution spectrogram,” ICASSP , 2020

2020

Later among the works it cites.

G. Yang, S. Yang, K. Liu, P. Fang, W. Chen, and L. Xie, “Multi-band melgan: Faster waveform generation for high-quality text-to-speech,” arXiv preprint arXiv:2005.0510 , 2020

Original

2020

Later among the works it cites.

M. Bińkowski, J. Donahue, S. Dieleman, A. Clark, E. Elsen, N. Casagrande, L. C. Cobo, and K. Simonyan, “High fidelity speech synthesis with adversarial networks,” ICLR , 2020

2020

Later among the works it cites.

Y. Song and S. Ermon, “Improved techniques for training score-based generative models,” arXiv preprint arXiv:2006.09011 , 2020

Original

2020

Later among the works it cites.