Fetching the paper…
Reading the bibliography…
In this work, we introduce NU-Wave, the first neural audio upsampling model to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs, while prior works could generate only up to 16kHz.
P. Vincent, “A connection between score matching and denoising autoencoders,” Neural computation , vol. 23, no. 7, pp. 1661–1674, 2011
2011
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,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
K. Li, Z. Huang, Y. Xu, and C.-H. Lee, “Dnn-based speech bandwidth expansion and its application to adding high-frequency missing features for automatic speech recognition of narrowband speech,” in INTERSPEECH , 2015, pp. 2578–2582
2015
Earlier work this paper cites.
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning , 2015, pp. 2256–2265
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Veaux, J. Yamagishi, K. MacDonald et al. , “Superseded-cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit(version 0.92),” 2016. [Online]. Available: https://datashare.ed.ac.uk/handle/10283/3443
2016
Earlier work this paper cites.
V. Kuleshov, S. Z. Enam, and S. Ermon, “Audio super resolution using neural networks,” in Workshop of International Conference on Learning Representations , 2017
2017
Earlier work this paper cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4681–4690
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
T. Y. Lim, R. A. Yeh, Y. Xu, M. N. Do, and M. Hasegawa-Johnson, “Time-frequency networks for audio super-resolution,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 646–650
2018
Cited alongside, same era.
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. Change Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops , 2018, pp. 0–0
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 , 2018, pp. 2410–2419
S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, “Pulse: Self-supervised photo upsampling via latent space exploration of generative models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2437–2445
2020
Later among the works it cites.
W. Ping, K. Peng, K. Zhao, and Z. Song, “Waveflow: A compact flow-based model for raw audio,” in International Conference on Machine Learning , 2020, pp. 7706–7716
2020
Later among the works it cites.
K. Peng, W. Ping, Z. Song, and K. Zhao, “Non-autoregressive neural text-to-speech,” in International Conference on Machine Learning , 2020, pp. 7586–7598
2020
Later among the works it cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems , 2020, pp. 6840–6851
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
X. Li, V. Chebiyyam, K. Kirchhoff, and A. Amazon, “Speech audio super-resolution for speech recognition.” in INTERSPEECH , 2019, pp. 3416–3420
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Birnbaum, V. Kuleshov, Z. Enam, P. W. W. Koh, and S. Ermon, “Temporal film: Capturing long-range sequence dependencies with feature-wise modulations.” in Advances in Neural Information Processing Systems , 2019, pp. 10 287–10 298
2019
Cited alongside, same era.
R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 3617–3621
2019
Cited alongside, same era.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” in Advances in Neural Information Processing Systems , 2019, pp. 11 918–11 930
2019
Cited alongside, same era.
N. Hou, C. Xu, V. T. Pham, J. T. Zhou, E. S. Chng, and H. Li, “Speaker and phoneme-aware speech bandwidth extension with residual dual-path network,” in INTERSPEECH , 2020, pp. 4064–4068
2020
Cited alongside, same era.
Y. Song and S. Ermon, “Improved techniques for training score-based generative models,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 12 438–12 448
2020
Later among the works it cites.
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, “Diffwave: A versatile diffusion model for audio synthesis,” in International Conference on Learning Representations , 2021
2021
Closest in time.
N. Chen, Y. Zhang, H. Zen, R. J. Weiss, M. Norouzi, and W. Chan, “Wavegrad: Estimating gradients for waveform generation,” in International Conference on Learning Representations , 2021
2021
Closest in time.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in International Conference on Learning Representations , 2021
2021
Closest in time.
A. Jolicoeur-Martineau, R. Piché-Taillefer, R. T. d. Combes, and I. Mitliagkas, “Adversarial score matching and improved sampling for image generation,” in International Conference on Learning Representations , 2021
2021
Closest in time.