The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
AudioLDM 2: Learning Holistic Audio Generation With Self-Supervised Pretraining
Open accessAbstract
Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration of specific objectives and biases that can significantly differ from those of other types. To bring us closer to a unified perspective of audio generation, this paper proposes a holistic framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework utilizes a general representation of audio, called “language of audio” (LOA). Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model. In the generation process, we translate other modalities into LOA by using a GPT-2 model, and we perform self-supervised audio generation learning with a latent diffusion model conditioned on the LOA of audio in our training set. The proposed framework naturally brings advantages such as reusable self-supervised pretrained latent diffusion models. Experiments on the major benchmarks of text-to-audio, text-to-music, and text-to-speech with three AudioLDM 2 variants demonstrate competitive performance of the AudioLDM 2 variants framework against previous approaches. Our code, pretrained model, and demo are available athttps://audioldm.github.io/audioldm2.
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Cites
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Denoising Diffusion Probabilistic Models
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Diffusion Models Beat GANs on Image Synthesis
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Audio Set: An ontology and human-labeled dataset for audio events
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Hierarchical Text-Conditional Image Generation with CLIP Latents
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“[39], [40], image restoration [41], speech generation [42], [43], [44], and video generation [45], [46].”
Background
Cited by
Mei, X., Meng, C., Liu, H., Kong, Q., Ko, T., Zhao, C., Plumbley, M. D., Zou, Y., & Wang, W. (2024) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2024.3419446 · Cited by 150
Self-citationA multifaceted vision of the human-AI collaboration: a comprehensive review
Puerta-Beldarrain, M., Gómez-Carmona, O., Sánchez-Corcuera, R., Casado-Mansilla, D., López-de-Ipiña, D., & Chen, L. (2025) · IEEE Access · doi:10.1109/access.2025.3536095 · Cited by 73
Sparks of Large Audio Models: A Survey and Outlook
Latif, S., Shoukat, M., Shamshad, F., Usama, M., Ren, Y., Cuayáhuitl, H., Wang, W., Zhang, X., Togneri, R., Cambria, E., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.12792 · Cited by 17
“Built on the success of AudioLDM, the authors have created a more advanced model called AudioLDM 2 [107], which aims to develop a general audio representation method called ”language of audio” (LOA), for speech, music and general sound effects.”
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Metadata
- Authors
- Liu, H., Yuan, Y., Liu, X., Mei, X., Kong, Q., Tian, Q., Wang, Y., Wang, W., Wang, Y., & Plumbley, M. D.
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2024
- Volume
- 32
- Pages
- 2871-2883
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W4396877837
- Cited by
- 180
- References
- 28
- FWCI
- 26.79
- Type
- journal article
- Language
- en
- Open access
- green
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