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Speech bandwidth extension (BWE) refers to widening the frequency bandwidth range of speech signals, enhancing the speech quality towards brighter and fuller.
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2016
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J. Sadasivan, S. Mukherjee, and C. S. Seelamantula, “Joint dictionary training for bandwidth extension of speech signals,” in Proc. ICASSP , 2016, pp. 5925–5929
2016
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2016
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2017
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Z.-H. Ling, Y. Ai, Y. Gu, and L.-R. Dai, “Waveform modeling and generation using hierarchical recurrent neural networks for speech bandwidth extension,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 26, no. 5, pp. 883–894, 2018
2018
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J. Abel, M. Strake, and T. Fingscheidt, “A simple cepstral domain DNN approach to artificial speech bandwidth extension,” in Proc. ICASSP , 2018, pp. 5469–5473
2018
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2021
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H. Liu, W. Choi, X. Liu, Q. Kong, Q. Tian, and D. Wang, “Neural vocoder is all you need for speech super-resolution,” in Proc. Interspeech , 2022, pp. 4227–4231
2022
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Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in Proc. CVPR , 2022, pp. 11 976–11 986
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2022
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W. Xiao, W. Liu, M. Wang, S. Yang, Y. Shi, Y. Kang, D. Su, S. Shang, and D. Yu, “Multi-mode neural speech coding based on deep generative networks,” in Proc. Interspeech , 2023, pp. 819–823
2023
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M. Mandel, O. Tal, and Y. Adi, “AERO: Audio super resolution in the spectral domain,” in Proc. ICASSP , 2023, pp. 1–5
2023
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C. Shuai, C. Shi, L. Gan, and H. Liu, “mdctGAN: Taming transformer-based GAN for speech super-resolution with modified DCT spectra,” in Proc. Interspeech , 2023, pp. 5112–5116
2023
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Y. Ai and Z.-H. Ling, “Neural speech phase prediction based on parallel estimation architecture and anti-wrapping losses,” in Proc. ICASSP , 2023, pp. 1–5
2023
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Y. Ai, Y.-X. Lu, and Z.-H. Ling, “Long-frame-shift neural speech phase prediction with spectral continuity enhancement and interpolation error compensation,” IEEE Signal Processing Letters , vol. 30, pp. 1097–1101, 2023
2023
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Y. Ai and Z.-H. Ling, “APNet: An all-frame-level neural vocoder incorporating direct prediction of amplitude and phase spectra,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 31, pp. 2145–2157, 2023
2023
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Y.-X. Lu, Y. Ai, and Z.-H. Ling, “MP-SENet: A speech enhancement model with parallel denoising of magnitude and phase spectra,” in Proc. Interspeech , 2023, pp. 3834–3838
2023
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C.-Y. Yu, S.-L. Yeh, G. Fazekas, and H. Tang, “Conditioning and sampling in variational diffusion models for speech super-resolution,” in Proc. ICASSP , 2023, pp. 1–5
2023
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2023
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A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” in Proc. ICML , 2023, pp. 28 492–28 518
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