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Speech super-resolution (SR) is a task to increase speech sampling rate by generating high-frequency components.
S. Chennoukh, A. Gerrits, G. Miet, and R. Sluijter, “Speech enhancement via frequency bandwidth extension using line spectral frequencies,” in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing , 2001, pp. 665–668
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P. Ekstrand, “Bandwidth extension of audio signals by spectral band replication,” in Proceedings of the IEEE Benelux Workshop on Model Based Processing and Coding of Audio . Citeseer, 2002
2002
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Y. Nakatoh, M. Tsushima, and T. Norimatsu, “Generation of broadband speech from narrowband speech based on linear mapping,” Electronics and Communications in Japan , vol. 85, no. 8, pp. 44–53, 2002
2002
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J. Kontio, L. Laaksonen, and P. Alku, “Neural network-based artificial bandwidth expansion of speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 15, no. 3, pp. 873–881, 2007
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A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in in ICML Workshop on Deep Learning for Audio, Speech and Language Processing , 2013
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K. Nakamura, K. Hashimoto, K. Oura, Y. Nankaku, and K. Tokuda, “A mel-cepstral analysis technique restoring high frequency components from low-sampling-rate speech,” in Conference of the International Speech Communication Association , 2014
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D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv:1412.6980 , 2014
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K. Li and C.-H. Lee, “A deep neural network approach to speech bandwidth expansion,” in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing , 2015, pp. 4395–4399
2015
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S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning , 2015, pp. 448–456
2015
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2017
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T. Y. Lim, R. A. Yeh, Y. Xu, M. N. Do, and M. Hasegawa-Johnson, “Time-frequency networks for audio super-resolution,” in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing , 2018, pp. 646–650
2018
Cited alongside, same era.
A. Gupta, B. Shillingford, Y. Assael, and T. C. Walters, “Speech bandwidth extension with wavenet,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics , 2019, pp. 205–208
2019
Cited alongside, same era.
S. E. Eskimez and K. Koishida, “Speech super resolution generative adversarial network,” in Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing , 2019, pp. 3717–3721
2019
Cited alongside, same era.
2020
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H. Wang and D. Wang, “Towards robust speech super-resolution,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 2058–2066, 2021
2021
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2021
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2021
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Q. Liu, P. J. Jackson, and W. Wang, “A speech synthesis approach for high quality speech separation and generation,” IEEE Signal Processing Letters , vol. 26, no. 12, pp. 1872–1876, 2019
2019
Cited alongside, same era.
J. Yamagishi, C. Veaux, K. MacDonald et al. , “CSTR VCTK corpus: English multi-speaker corpus for cstr voice cloning toolkit,” 2019
2019
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
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Q. Kong, Y. Cao, H. Liu, K. Choi, and Y. Wang, “Decoupling magnitude and phase estimation with deep resunet for music source separation.” in The International Society for Music Information Retrieval , 2021
2021
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2021
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2021
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X. Liu, T. Iqbal, J. Zhao, Q. Huang, M. D. Plumbley, and W. Wang, “Conditional sound generation using neural discrete time-frequency representation learning,” in IEEE Workshop on Machine Learning for Signal Processing , 2021, pp. 1–6
2021
Later among the works it cites.