[17] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34, 2021.
[18] Rongjie Huang, Max WY Lam, Jun Wang, Dan Su, Dong Yu, Yi Ren, and Zhou Zhao. Fast-diff: A fast conditional diffusion model for high-quality speech synthesis. arXiv preprint arXiv:2204.09934, 2022.
[19] Sungwon Kim, Heeseung Kim, and Sungroh Yoon. Guided-tts 2: A diffusion model for high-quality adaptive text-to-speech with untranscribed data. arXiv preprint arXiv:2205.15370, 2022.
[20] Kai Shen, Zeqian Ju, Xu Tan, Yanqing Liu, Yichong Leng, Lei He, Tao Qin, Sheng Zhao, and Jiang Bian. Naturalspeech 2: Latent diffusion models are natural and zero-shot speech and singing synthesizers. arXiv preprint arXiv:2304.09116, 2023.
[21] Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley. Audioldm: Text-to-audio generation with latent diffusion models. arXiv preprint arXiv:2301.12503, 2023.
[22] Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, and Zhou Zhao. Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models. arXiv preprint arXiv:2301.12661, 2023.
[23] Flavio Schneider, Zhijing Jin, and Bernhard Scholkopf. Mousai: Text-to-music generation with long-context latent diffusion. arXiv preprint arXiv:2301.11757, 2023.
[24] David Holz et al. Midjourney. Artificial Intelligence platform. Accessible at https://www.midjourney.com/Accessed November 1st, 2023.
[25] Zhisheng Xiao, Karsten Kreis, and Arash Vahdat. Tackling the generative learning trilemma with denoising diffusion gans. arXiv preprint arXiv:2112.07804, 2021.
[26] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.
[27] Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D Plumbley. Panns: Large-scale pretrained audio neural networks for audio pattern recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28:2880-2894, 2020.
[28] Antoine Caillon and Philippe Esling. Rave: A variational autoencoder for fast and high-quality neural audio synthesis. arXiv preprint arXiv:2111.05011, 2021.
[29] Marco Pasini and Jan Schlüter. Musika! fast infinite waveform music generation. arXiv preprint arXiv:2208.08706, 2022.
[30] Mubert Inc. Mubert. URL https://mubert.com/, 2023.
[31] S Forsgren and H Martiros. Diffusion - stable diffusion for real-time music generation. URL https://riffusion.com/, 2023.
[32] Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 776-780. IEEE, 2017.
[33] Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499, 2016.
[34] Aaron Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George Driessche, Edward Lockhart, Luis Cobo, Florian Stimberg, et al. Parallel wavenet: Fast high-fidelity speech synthesis. In International conference on machine learning, pages 3918-3926. PMLR, 2018.