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We introduce EzAudio, a text-to-audio (T2A) generation framework designed to produce high-quality, natural-sounding sound effects.
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 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2017, pp. 776–780
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 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 119–132
2019
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,” IEEE/ACM 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.
S. Hershey, D. P. Ellis, E. Fonseca, A. Jansen, C. Liu, R. C. Moore, and M. Plakal, “The benefit of temporally-strong labels in audio event classification,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 366–370
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Earlier work this paper cites.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International conference on machine learning . PMLR, 2022, pp. 12 888–12 900
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
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 International Conference on Machine Learning . PMLR, 2023, pp. 21 450–21 474
2023
Earlier work this paper cites.
R. Huang, J. Huang, D. Yang, Y. Ren, L. Liu, M. Li, Z. Ye, J. Liu, X. Yin, and Z. Zhao, “Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models,” in International Conference on Machine Learning . PMLR, 2023, pp. 13 916–13 932
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4195–4205
2023
Cited alongside, same era.
2023
Cited alongside, same era.
S. Lin, B. Liu, J. Li, and X. Yang, “Common diffusion noise schedules and sample steps are flawed,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2024, pp. 5404–5411
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang, M. Dehghani, S. Brahma et al. , “Scaling instruction-finetuned language models,” Journal of Machine Learning Research , vol. 25, no. 70, pp. 1–53, 2024
2024
Closest in time.
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F. Bao, S. Nie, K. Xue, Y. Cao, C. Li, H. Su, and J. Zhu, “All are worth words: A vit backbone for diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 22 669–22 679
2023
Cited alongside, same era.
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 ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Cited alongside, same era.
S. Gao, P. Zhou, M.-M. Cheng, and S. Yan, “Masked diffusion transformer is a strong image synthesizer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 23 164–23 173
2023
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
J. Chen, J. Yu, C. Ge, L. Yao, E. Xie, Z. Wang, J. T. Kwok, P. Luo, H. Lu, and Z. Li, “Pixart- α \alpha : Fast training of diffusion transformer for photorealistic text-to-image synthesis,” in The Twelfth International Conference on Learning Representations , 2024
2024
Cited alongside, same era.
X. Mei, C. Meng, H. Liu, Q. Kong, T. Ko, C. Zhao, M. D. Plumbley, Y. Zou, and W. Wang, “Wavcaps: A chatgpt-assisted weakly-labelled audio captioning dataset for audio-language multimodal research,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2024
2024
Cited alongside, same era.
M. Le, A. Vyas, B. Shi, B. Karrer, L. Sari, R. Moritz, M. Williamson, V. Manohar, Y. Adi, J. Mahadeokar et al. , “Voicebox: Text-guided multilingual universal speech generation at scale,” Advances in neural information processing systems , vol. 36, 2024
2024
Closest in time.
L. Sun, X. Xu, M. Wu, and W. Xie, “Auto-ACD: A large-scale dataset for audio-language representation learning,” in ACM Multimedia 2024 , 2024
2024
Closest in time.
D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rombach, “Sdxl: Improving latent diffusion models for high-resolution image synthesis,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
J. Hai, H. Wang, D. Yang, K. Thakkar, N. Dehak, and M. Elhilali, “Dpm-tse: A diffusion probabilistic model for target sound extraction,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 1196–1200
2024
Closest in time.
H. Liu, Y. Yuan, X. Liu, X. Mei, Q. Kong, Q. Tian, Y. Wang, W. Wang, Y. Wang, and M. D. Plumbley, “Audioldm 2: Learning holistic audio generation with self-supervised pretraining,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2024
2024
Closest in time.
J. Su, M. Ahmed, Y. Lu, S. Pan, W. Bo, and Y. Liu, “Roformer: Enhanced transformer with rotary position embedding,” Neurocomputing , vol. 568, p. 127063, 2024
2024
Closest in time.