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Diffusion models are instrumental in text-to-audio (TTA) generation.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning , 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention , 2015
2015
Earlier work this paper cites.
B. McFee, “ResamPy: efficient sample rate conversion in python,” Journal of Open Source Software , vol. 1, no. 8, p. 125, 2016
2016
Earlier work this paper cites.
S. Hershey, S. Chaudhuri, D. P. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold et al. , “CNN architectures for large-scale audio classification,” in International Conference on Acoustics, Speech and Signal Processing , 2017
2017
Earlier work this paper cites.
J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio Set: An ontology and human-labeled dataset for audio events,” in International Conference on Acoustics, Speech and Signal Processing , 2017
2017
Earlier work this paper cites.
C. D. Kim, B. Kim, H. Lee, and G. Kim, “AudioCaps: Generating captions for audios in the wild,” in Conference of the North American Chapter of the Association for Computational Linguistics , 2019
2019
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems , 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Kong, J. Kim, and J. Bae, “HiFi-GAN: Generative adversarial networks for efficient and high fidelity speech synthesis,” in Advances in Neural Information Processing Systems , 2020
2020
Earlier work this paper cites.
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,” Transactions on Audio, Speech, and Language Processing , vol. 28, pp. 2880–2894, 2020
2020
Earlier work this paper cites.
J. Ho and T. Salimans, “Classifier-free diffusion guidance,” in NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications , 2021
2021
Earlier work this paper cites.
F. Bao, C. Li, J. Zhu, and B. Zhang, “Analytic-DPM: an analytic estimate of the optimal reverse variance in diffusion probabilistic models,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
T. Salimans and J. Ho, “Progressive distillation for fast sampling of diffusion models,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
S. Forsgren and H. Martiros, “Riffusion - stable diffusion for real-time music generation,” URL https://riffusion.com , 2022
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Conference on Computer Vision and Pattern Recognition , 2022
2022
Cited alongside, same era.
T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” in Advances in Neural Information Processing Systems , 2022
2022
Cited alongside, same era.
C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “DPM-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps,” in Advances in Neural Information Processing Systems , 2022
2022
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
F. Kreuk, G. Synnaeve, A. Polyak, U. Singer, A. Défossez, J. Copet, D. Parikh, Y. Taigman, and Y. Adi, “AudioGen: Textually guided audio generation,” in International Conference on Learning Representations , 2023
2023
Closest in time.
Y. Song, P. Dhariwal, M. Chen, and I. Sutskever, “Consistency models,” in International Conference on Machine Learning , 2023
2023
Closest in time.
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2022
Cited alongside, same era.
L. Liu, Y. Ren, Z. Lin, and Z. Zhao, “Pseudo numerical methods for diffusion models on manifolds,” in International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
R. Huang, M. W. Lam, J. Wang, D. Su, D. Yu, Y. Ren, and Z. Zhao, “Fastdiff: A fast conditional diffusion model for high-quality speech synthesis,” in International Joint Conference on Artificial Intelligence , 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
D. Yang, J. Yu, H. Wang, W. Wang, C. Weng, Y. Zou, and D. Yu, “Diffsound: Discrete diffusion model for text-to-sound generation,” Transactions on Audio, Speech, and Language Processing , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
B. Elizalde, S. Deshmukh, M. Al Ismail, and H. Wang, “CLAP: learning audio concepts from natural language supervision,” in International Conference on Acoustics, Speech and Signal Processing , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
C. Meng, R. Rombach, R. Gao, D. Kingma, S. Ermon, J. Ho, and T. Salimans, “On distillation of guided diffusion models,” in Conference on Computer Vision and Pattern Recognition , 2023
2023
Closest in time.
2023
Closest in time.
Y. Wu, K. Chen, T. Zhang, Y. Hui, T. Berg-Kirkpatrick, and S. Dubnov, “Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,” in International Conference on Acoustics, Speech and Signal Processing , 2023
2023
Closest in time.