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Audio super resolution aims to predict the missing high resolution components of the low resolution audio signals.
“Communication in the presence of noise,”
C.E. Shannon, · 1949
Earlier work this paper cites.
Discrete-Time Signal Processing
Alan V. Oppenheim, Ronald W. Schafer, and John R. Buck, · 1999
Earlier work this paper cites.
“Design of digital systems for arbitrary sampling rate conversion,”
Gennaro Evangelista, · 2003
Earlier work this paper cites.
“Artificial bandwidth extension of speech signals using mmse estimation based on a hidden markov model,”
P. Jax and P. Vary, · 2003
Earlier work this paper cites.
“Speech bandwidth extension using gaussian mixture model-based estimation of the highband mel spectrum,”
Hannu Pulakka, Ulpu Remes, Kalle Palomäki, Mikko Kurimo, and Paavo Alku, · 2011
Earlier work this paper cites.
“Sample rate conversion using b-spline interpolation for ofdm based software defined radios,”
Xiaojing Huang, Y. Jay Guo, and Jian Andrew Zhang, · 2012
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik Kingma and Jimmy Ba, · 2015
Earlier work this paper cites.
“CSTR VCTK Corpus: English multi-speaker corpus for cstr voice cloning toolkit,” 2016
Christophe Veaux, Junichi Yamagishi, and Kirsten MacDonald, · 2016
Cited alongside, same era.
“Audio super resolution using neural networks,”
Volodymyr Kuleshov, S Zayd Enam, and Stefano Ermon, · 2017
Cited alongside, same era.
“Efficient super-wide bandwidth extension using linear prediction based analysis-synthesis,”
Pramod Bachhav, Massimiliano Todisco, and Nicholas Evans, · 2018
Cited alongside, same era.
“Adversarial training for speech super-resolution,”
S. E. Eskimez, K. Koishida, and Z. Duan, · 2019
Cited alongside, same era.
“Deepsdf: Learning continuous signed distance functions for shape representation,”
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove, · 2019
Cited alongside, same era.
“Nerf: Representing scenes as neural radiance fields for view synthesis,”
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng, · 2020
Later among the works it cites.
“Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram,”
Ryuichi Yamamoto, Eunwoo Song, and Jae-Min Kim, · 2020
Later among the works it cites.
“Bandwidth extension is all you need,”
Jiaqi Su, Yunyun Wang, Adam Finkelstein, and Zeyu Jin, · 2021
Closest in time.
“Wsrglow: A glow-based waveform generative model for audio super-resolution,”
Kexun Zhang, Yi Ren, Changliang Xu, and Zhou Zhao, · 2021
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“Learning continuous image representation with local implicit image function,”
Yinbo Chen, Sifei Liu, and Xiaolong Wang, · 2021
Closest in time.
“Modulated periodic activations for generalizable local functional representations,”
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“Temporal film: Capturing long-range sequence dependencies with feature-wise modulation,”
Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, and Stefano Ermon, · 2019
Cited alongside, same era.
“Implicit neural representations with periodic activation functions,”
Vincent Sitzmann, Julien N.P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein, · 2020
Cited alongside, same era.
Ishit Mehta, Michaël Gharbi, Connelly Barnes, Eli Shechtman, Ravi Ramamoorthi, and Manmohan Chandraker, · 2021
Closest in time.