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Audio bandwidth extension involves the realistic reconstruction of high-frequency spectra from bandlimited observations.
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H. Seo, H.-G. Kang, and F. Soong, “A maximum a posterior-based reconstruction approach to speech bandwidth expansion in noise,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Florence, Italy, May 2014, pp. 6087–6091
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K. Li and C.-H. Lee, “A deep neural network approach to speech bandwidth expansion,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , South Brisbane, QLD, Australia, Apr. 2015, pp. 4395–4399
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J. Abel, M. Kaniewska, C. Guillaume, W. Tirry, H. Pulakka, V. Myllylä, J. Sjöberg, P. Alku, I. Katsir, D. Malah, I. Cohen, M. A. Tugtekin Turan, E. Erzin, T. Schlien, P. Vary, A. Nour-Eldin, P. Kabal, and T. Fingscheidt, “A subjective listening test of six different artificial bandwidth extension approaches in English, Chinese, German, and Korean,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Shanghai, China, Mar. 2016, pp. 5915–5919
2016
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V. K., S. Z. Enam, and S. Ermon, “Audio super resolution using neural nets,” in Proc. Int. Conf. Learn. Represent. (ICLR) (Workshop Track) , Toulon, France, Apr. 2017
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M. Miron and M. Davies, “High frequency magnitude spectrogram reconstruction for music mixtures using convolutional autoencoders,” in Proc. Int. Conf. Digit. Audio Effects , Aveiro, Portugal, Sep. 2018, pp. 173–180
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N. Pretto, N. D. Pozza, A. Padoan, A. Chmiel, K. J. Werner, A. Micalizzi, E. Schubert, A. Roda, S. Milani, and S. Canazza, “A workflow and digital filters for correcting speed and equalization errors on digitized audio open-reel magnetic tapes,” J. Audio Eng. Soc. , vol. 70, no. 6, pp. 495–509, Jun. 2022
2018
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A. Gupta, B. Shillingford, Y. Assael, and T. C. Walters, “Speech bandwidth extension with wavenet,” in Proc. IEEE Work. App. Signal Process. Audio Acoust. (WASPAA) , New Paltz, NY, USA, Oct. 2019, pp. 205–208
2019
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S. E. Eskimez, K. Koishida, and Z. Duan, “Adversarial training for speech super-resolution,” IEEE J. Sel. Topics Signal Process. , vol. 13, no. 2, pp. 347–358, Apr. 2019
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C. Hawthorne, A. Stasyuk, A. Roberts et al. , “Enabling factorized piano music modeling and generation with the MAESTRO dataset,” in Proc. Int. Conf. Learning Representations (ICLR) , May 2019
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K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi, “Fréchet audio distance: A reference-free metric for evaluating music enhancement algorithms,” in Proc. Interspeech , Aug. 2019, pp. 2350–2354
2019
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M. Lagrange and F. Gontier, “Bandwidth extension of musical audio signals with no side information using dilated convolutional neural networks,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Barcelona, Spain, May 2020, pp. 801–805
2020
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S. Sulun and M. E. P. Davies, “On filter generalization for music bandwidth extension using deep neural networks,” IEEE J. Sel. Topics Signal Process. , vol. 15, no. 1, pp. 132–142, Nov. 2020
2020
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H. Chung, B. Sim, and J. C. Ye, “Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 12 413–12 422
2022
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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 , Incheon, Korea, Aug. 2022
2022
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E. Moliner and V. Välimäki, “A two-stage U-net for high-fidelity denoising of historical recordings,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Singapore, May 2022, pp. 841–845
2022
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J. Ho, T. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet, “Video diffusion models,” Adv. Neural Inf. Process. Syst. (NeurIPS) , Dec. 2022
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Adv. Neural Inf. Process. Syst. (NeurIPS) , pp. 6840–6851, Dec. 2020
2020
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Q. Kong, Y. Cao, T. Iqbal, Y. Wang, W. Wang, and M. D. Plumbley, “PANNs: Large-scale pretrained audio neural networks for audio pattern recognition,” IEEE/ACM Trans. Audio Speech Lang. Process. , vol. 28, pp. 2880–2894, Nov. 2020
2020
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S. Sulun and M. E. Davies, “On filter generalization for music bandwidth extension using deep neural networks,” IEEE J. Sel. Top. Signal Process. , vol. 15, no. 1, pp. 132–142, Nov. 2020
2020
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K. Zhang, Y. Ren, C. Xu, and Z. Zhao, “WSRGlow: A Glow-based waveform generative model for audio, super-resolution,” in Proc. Interspeech , Shanghai, China, Aug. 2021, pp. 1649–1653
2021
Cited alongside, same era.
K. Schmidt and B. Edler, “Blind bandwidth extension of speech based on LPCNet,” in Proc. 28th European Signal Process. Conf. (EUSIPCO) , Aug. 2021, pp. 426–430
2021
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2021
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H. Wang and D. Wang, “Towards robust speech super-resolution,” IEEE/ACM Trans. Audio Speech Lang. Process. , vol. 29, pp. 2058–2066, Jan. 2021
2021
Cited alongside, same era.
J. Su, Y. Wang, A. Finkelstein, and Z. Jin, “Bandwidth extension is all you need,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Toronto, Canada, Jun. 2021, pp. 696–700
2021
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2022
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Y. Wu, E. Manilow, Y. Deng, R. Swavely, K. Kastner, T. Cooijmans, A. Courville, C.-Z. A. Huang, and J. Engel, “MIDI-DDSP: Detailed control of musical performance via hierarchical modeling,” in Proc. Int. Conf. Learning Representations (ICLR) , online, Apr. 2022
2022
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E. Moliner and V. Välimäki, “BEHM-GAN: Bandwidth extension of historical music using generative adversarial networks,” IEEE/ACM Trans. Audio Speech Lang. Process. , vol. 31, pp. 943–956, Jul. 2023
2023
Closest in time.
E. Moliner, J. Lehtinen, and V. Välimäki, “Solving audio inverse problems with a diffusion model,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Rhodes, Greece, Jun. 2023
2023
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P. Andreev, A. Alanov, O. Ivanov, and D. Vetrov, “HiFi++: A unified framework for bandwidth extension and speech enhancement,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Rhodes, Greece, Jun. 2023
2023
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J.-M. Lemercier, J. Richter, S. Welker, and T. Gerkmann, “Analysing diffusion-based generative approaches versus discriminative approaches for speech restoration,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Rhodes, Greece, Jun. 2023
2023
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M. Mandel, O. Tal, and Y. Adi, “AERO: Audio super resolution in the spectral domain,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Rhodes, Greece, Jun. 2023
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. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Rhodes, Greece, Jun. 2023
2023
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2023
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2023
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H. Yen, F. G. Germain, G. Wichern, and J. L. Roux, “Cold diffusion for speech enhancement,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Rhodes, Greece, Jun. 2023
2023
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2023
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H. Chung, J. Kim, S. Kim, and J. C. Ye, “Parallel diffusion models of operator and image for blind inverse problems,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) , Vancouver, BC, Canada, Jun. 2023, pp. 6059–6069
2023
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2023
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H. Chung, J. Kim, M. T. Mccann et al. , “Diffusion posterior sampling for general noisy inverse problems,” in Proc. Int. Conf. Learning Representations (ICLR) , Kigali, Rwanda, May 2023
2023
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H. Liu, Z. Chen, Y. Yuan, X. Mei, X. Liu, D. Mandic, W. Wang, and M. D. Plumbley, “AudioLDM: Text-to-audio generation with latent diffusion models,” in in Proc. Int. Conf. Machine Learning (ICML) , Honolulu, Hawaii, USA, Jul. 2023
2023
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S. Pascual, G. Bhattacharya, C. Yeh, J. Pons, and J. Serrà, “Full-band general audio synthesis with score-based diffusion,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Rhodes, Greece, Jun. 2023
2023
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E. Moliner and V. Välimäki, “Diffusion-based audio inpainting,” J. Audio Eng. Soc. , vol. 72, Mar. 2024
2024
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